[go: up one dir, main page]

 
 
Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Journals

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Article Types

Countries / Regions

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Search Results (3,562)

Search Parameters:
Keywords = chemical kinetic

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
17 pages, 3430 KiB  
Systematic Review
Liquid Organic Hydrogen Carrier Concepts and Catalysts for Hydrogenation and Dehydrogenation Reactions
by Gerardo Cabrera, Malka Mora, Juan P. Gil-Burgos, Renso Visbal, Fiderman Machuca-Martínez and Edgar Mosquera-Vargas
Molecules 2024, 29(20), 4938; https://doi.org/10.3390/molecules29204938 (registering DOI) - 18 Oct 2024
Abstract
Background: The issue of renewable energy (RE) source intermittency, such as wind and solar, along with the geographically uneven distribution of the global RE potential, makes it imperative to establish an energy transport medium to balance the energy demand and supply areas. A [...] Read more.
Background: The issue of renewable energy (RE) source intermittency, such as wind and solar, along with the geographically uneven distribution of the global RE potential, makes it imperative to establish an energy transport medium to balance the energy demand and supply areas. A promising energy vector to address this situation is hydrogen, which is considered a clean energy carrier for various mobile and portable applications. Unfortunately, at standard pressure and temperature, its energy content per volume is very low (0.01 kJ/L). This necessitates alternative storage technologies to achieve reasonable capacities and enable economically viable long-distance transportation. Among the hydrogen storage technologies using chemical methods, liquid organic hydrogen carrier (LOHC) systems are considered a promising solution. They can be easily managed under ambient conditions, the H2 storage/release processes are carbon-free, and the carrier liquid is reusable. However, the evolution of the proposals from the carrier liquid type and catalyst elemental composition point of view is scarcely studied, considering that both are critical in the performance of the system (operational parameters, kinetic of the reactions, gravimetric hydrogen content, and others) and impact in the final cost of the technology deployed. The latter is due to the use of the Pt group elements (PGEs) in the catalyst that, for example, have a high demand in the hydrogen production sector, particularly for polymer electrolyte membrane (PEM) water electrolysis. With that in mind, our objective was to examine the evolution and the focus of the research in recent years related to proposals of LOHCs and catalysts for hydrogenation and dehydrogenation reactions in LOHC systems which can be useful in defining routes/strategies for new participants interested in becoming involved in the development of this technology. Data sources: For this systematic review, we searched the SCOPUS database and forward and backward citations for studies published in the database between January 2011 and December 2022. Eligibility criteria: The criteria include articles which assessed or studied the effect of the type of catalyst, type of organic liquid, reactor design(s)/configuration(s), and modification of the reactor operational parameters, among others, over the performance of the LOHC system (de/hydrogenation reaction(s)). Data extraction and analysis: The relevant data from each reviewed study were collected and organized into a pre-designed table on an Excel spreadsheet, categorized by reference, year, carrier organic liquid, reaction (hydrogenation and/or dehydrogenation), investigated catalyst, and primary catalyst element. For processing the data obtained from the selected scientific publications, the data analysis software Orbit Intellixir was employed. Results: For the study, 233 studies were included. For the liquid carrier side, benzyltoluene and carbazole dominate the research strategies. Meanwhile, platinum (Pt) and palladium (Pd) are the most employed catalysts for dehydrogenation reactions, while ruthenium (Ru) is preferred for hydrogenation reactions. Conclusions: From the investigated liquid carrier, those based on benzyltoluene and carbazole together account for over 50% of the total scientific publications. Proposals based on indole, biphenyl, cyclohexane, and cyclohexyl could be considered to be emerging within the time considered in this review, and, therefore, should be monitored for their evolution. A great activity was detected in the development of catalysts oriented toward the dehydrogenation reaction, because this reaction requires high temperatures and presents slow H2 release kinetics, conditioning the success of the implementation of the technology. Finally, from the perspective of the catalyst composition (monometallic and/or bimetallic), it was identified that, for the dehydrogenation reaction, the most used elements are platinum (Pt) and palladium (Pd), while, for the hydrogenation reaction, ruthenium (Ru) widely leads its use in the different catalyst designs. Therefore, the near-term initiatives driving progress in this field are expected to focus on the development of new or improved catalysts for the dehydrogenation reaction of organic liquids based on benzyltoluene and carbazole. Full article
Show Figures

Figure 1

Figure 1
<p>Concept/descriptor graph over time for the topic of organic liquids (extracted from Intellixir software (<a href="https://carlac.intellixir.fr/cenm" target="_blank">https://carlac.intellixir.fr/cenm</a>, accessed on 16 October 2024) based on the 233 selected publications).</p>
Full article ">Figure 2
<p>Percentage distribution for the top 5 investigated organic liquids. Source: Own elaboration based on data from the 233 selected publications.</p>
Full article ">Figure 3
<p>Common aromatic <span class="html-italic">N</span>–heterocyclic compounds used in LOHC systems.</p>
Full article ">Figure 4
<p>Common homocyclic aromatic compounds used in LOHC systems.</p>
Full article ">Figure 5
<p>Temporal concept/descriptor graph for the topic of hydrogenation and dehydrogenation reactions, generated from Intellixir software based on 233 selected publications.</p>
Full article ">Figure 6
<p>Distribution of elements in monometallic catalysts used in (<b>a</b>) dehydrogenation reactions and (<b>b</b>) hydrogenation reactions. “Several” indicates that the study evaluates various monometallic catalysts. Source: Own elaboration based on data from 233 selected publications.</p>
Full article ">Figure 7
<p>Distribution of elements in bimetallic catalysts used in (<b>a</b>) dehydrogenation reaction, and (<b>b</b>) hydrogenation reactions. Source: Own elaboration based on data from the 233 selected publications.</p>
Full article ">Figure 7 Cont.
<p>Distribution of elements in bimetallic catalysts used in (<b>a</b>) dehydrogenation reaction, and (<b>b</b>) hydrogenation reactions. Source: Own elaboration based on data from the 233 selected publications.</p>
Full article ">Scheme 1
<p>Classification of hydrogen storage technologies.</p>
Full article ">Scheme 2
<p>Flow diagram of the systematic review study selection process. Adapted from Ref. [<a href="#B76-molecules-29-04938" class="html-bibr">76</a>].</p>
Full article ">
14 pages, 1213 KiB  
Article
Impact of High-Pressure Processing on Quality and Safety of High-Oil-Content Pesto Sauce: A Comparative Study with Thermal Processing
by Ehsan Shad, Kaisa Raninen, Svetlana Podergina, Lok In Chan, Kam Pui Tong, Heidi Hälikkä, Marjo Huovinen and Jenni Korhonen
Appl. Sci. 2024, 14(20), 9425; https://doi.org/10.3390/app14209425 - 16 Oct 2024
Viewed by 357
Abstract
High-pressure processing (HPP) is a promising technology for increasing the shelf life of food, with minimal effects on the nutritional or sensory quality. However, there has been a concern that high-oil-content foods may protect food pathogens in HPP, and that HPP can affect [...] Read more.
High-pressure processing (HPP) is a promising technology for increasing the shelf life of food, with minimal effects on the nutritional or sensory quality. However, there has been a concern that high-oil-content foods may protect food pathogens in HPP, and that HPP can affect the quality of lipids. We inoculated Listeria monocytogenes and Salmonella Typhimurium into 34% and 54% oil-content pesto sauce, processed them either with HPP (600 MPa, 4 min) or thermal processing (82 °C, 5 min), and analyzed bacteria counts, pH, GC-MS (Terpene compounds), the time–kill kinetic study, and lipid oxidation value for 60 days in refrigerating storage (5 ± 2 °C). Our findings show that HPP significantly reduced the number of bacteria (more than 4-log) compared to thermal processing or non-processing. Additionally, we discovered terpene compounds (highest-level terpene: L-linalool, eugenol, and 1,8-cineol) in pesto oil that exhibit antimicrobial activity. Different oil content did not have any significant effect on bacteria levels. Regarding chemical results, all samples were of acceptable quality, and the processes did not show any negative effect on lipid oxidation (Peroxide and P-Anisidine value under 10 meq per kilogram of oil). In conclusion, our study indicates that HPP is a suitable method for high-oil-content pesto sauce. In addition, functional compounds naturally present in pesto may contribute to maintaining its microbial and chemical quality. Full article
Show Figures

Figure 1

Figure 1
<p><span class="html-italic">Listeria monocytogenes</span> counts in the pesto samples having 34 or 54% oil content and different processing methods on day 1 (<b>a</b>) and during 60 days of storage (<b>b</b>). Data represent the mean ± standard deviation of three independent replicates; different superscript letters in each column indicate significant differences (<span class="html-italic">p</span> &lt; 0.05). LNP: non-processed samples with <span class="html-italic">L. monocytogenes,</span> LTP: thermal-processed samples with <span class="html-italic">L. monocytogenes,</span> LHP: HPP samples with <span class="html-italic">L. monocytogenes.</span> LOD: limit of detection (2 log CFU/g).</p>
Full article ">Figure 2
<p>Effect of HPP and thermal processing on pH in pesto sauces. Data represent the mean ± standard deviation of three independent replicates; different superscript letters in each column indicate significant differences (<span class="html-italic">p</span> &lt; 0.05). NP-34: 34% oil-content non-processed sample, HPP-34: 34% oil-content HPP sample, TP-34: 34% oil-content thermal-processed sample, NP-54: 54% oil-content non-processed sample, HPP-54: 54% oil-content HPP sample, TP-54: 54% oil-content thermal-processed sample.</p>
Full article ">Figure 3
<p>Effect of HPP and thermal processing on lipid oxidation in pesto sauce. (<b>a</b>) Peroxide value results in day 1 (solid fill) and 60 (pattern fill) of storage time, (<b>b</b>) p-anisidine value results after 60 days storage time. Data represent the mean ± standard deviation of three independent replicates; different superscript letters in each column indicate significant differences (<span class="html-italic">p</span> &lt; 0.05, Same letters mean no significant differences). NP-34: 34% oil-content non-processed sample, HPP-34: 34% oil-content HPP sample, TP-34: 34% oil-content thermal-processed sample, NP-54: 54% oil-content non-processed sample, HPP-54: 54% oil-content HPP sample, TP-54: 54% oil-content thermal-processed sample.</p>
Full article ">
14 pages, 17999 KiB  
Article
Thermal and Moisture Content Monitoring of a Full-Scale Load Bearing Hemp Lime Arch Prototype
by Arthur Bohn and Andrea Bocco
Sustainability 2024, 16(20), 8912; https://doi.org/10.3390/su16208912 - 15 Oct 2024
Viewed by 319
Abstract
Today, bio-sourced materials represent an important technological field of study, as they could sink atmospheric carbon dioxide into buildings. Little-processed construction materials would also reduce the environmental impact of the construction sector, which emitted more than 2.9 Mt of CO2 in 2020. [...] Read more.
Today, bio-sourced materials represent an important technological field of study, as they could sink atmospheric carbon dioxide into buildings. Little-processed construction materials would also reduce the environmental impact of the construction sector, which emitted more than 2.9 Mt of CO2 in 2020. Hemp-lime is a material that meets both these requirements. It is an insulating mix that can take different forms and be used in various parts of a building. The challenge is providing it with enough mechanical strength to make it loadbearing, at least to some extent. This research focuses on the construction and monitoring of a pointed arch, based on a previous experimental hemp-lime construction at Cardiff University in 2009, under the direction of architect David Lea. Since 2022, such an experiment on a possible loadbearing hemp-lime mix is being repeated at the Politecnico di Torino as part of a wider project called “experimental pavilions of vegetarian architecture”. The design and numerical analysis of the Cardiff prototype led to the modification of both the geometry and the composition of the mix using only pozzolanic air lime as the binder. The construction of the arch ended in December 2023. Observing the thermo-hygrometric conditions of this hemp-lime mix once in place is the main purpose of this article. A strong correlation is revealed between outdoor conditions with temperature and moisture content in the core of the arch. Building a full-size outdoor prototype allows for the avoidance of mathematical correction to the results obtained and allows the assessment the mix’s resistance in relation with environmental conditions. Due to some similarities of nature and function between lime and cement, many studies of lime mixes do not exceed a duration of 28 days, which cannot be considered the appropriate observation time for its curing. Therefore, we analysed this lime-based material for around 6 months, according to its own temporality and chemical kinetics. Through continuous monitoring at 10-min intervals, it was possible to highlight several significant aspects of rammed hemp-lime. The results show that the temperature within the mix is influenced by the outside temperature, but the sun exposure of certain areas drives up the corresponding temperature values more rapidly. Furthermore, while the absorption of water in the form of vapour is very rapid, desorption takes longer, as does re-establish a balance between the material and its context. Finally, solar exposure affects particularly 30-cm-thick elements, while elements that are 60 cm thick are not affected in the short term but only in long-term exposure conditions like season changes. Full article
Show Figures

Figure 1

Figure 1
<p>Data acquisition system.</p>
Full article ">Figure 2
<p>Position of sensors: moisture content (A~E′)—temperature (W~Z).</p>
Full article ">Figure 3
<p>Proportion of hemp shives with respect to lime: (<b>a</b>) hemp-lime dry mass composition and (<b>b</b>) hemp-lime dry volume composition.</p>
Full article ">Figure 4
<p>Details of the formwork building process: (<b>a</b>) making the formwork units and (<b>b</b>) design of the entire formwork.</p>
Full article ">Figure 5
<p>Homemade electric tamper.</p>
Full article ">Figure 6
<p>Pictures showing the work of placing the probes: (<b>a</b>) embedding the probes and (<b>b</b>) welding the hardware system.</p>
Full article ">Figure 7
<p>Life-size prototype at the Grugliasco experimental site.</p>
Full article ">Figure 8
<p>Temperature curves for the setting period.</p>
Full article ">Figure 9
<p>Moisture content and rainfall plots.</p>
Full article ">Figure 10
<p>Moisture content linear regression.</p>
Full article ">Figure 11
<p>Relative moisture content and rainfall at the beginning of the setting.</p>
Full article ">Figure 12
<p>Mixture sorption behaviour.</p>
Full article ">Figure 13
<p>Moisture desorption behaviour.</p>
Full article ">Figure 14
<p>Measurement of wind in April 2024.</p>
Full article ">
17 pages, 1446 KiB  
Article
Cell Cycle Complexity: Exploring the Structure of Persistent Subsystems in 414 Models
by Stephan Peter, Arun Josephraj and Bashar Ibrahim
Biomedicines 2024, 12(10), 2334; https://doi.org/10.3390/biomedicines12102334 - 14 Oct 2024
Viewed by 405
Abstract
Background: The regulation of cellular proliferation and genomic integrity is controlled by complex surveillance mechanisms known as cell cycle checkpoints. Disruptions in these checkpoints can lead to developmental defects and tumorigenesis. Methods: To better understand these mechanisms, computational modeling has been [...] Read more.
Background: The regulation of cellular proliferation and genomic integrity is controlled by complex surveillance mechanisms known as cell cycle checkpoints. Disruptions in these checkpoints can lead to developmental defects and tumorigenesis. Methods: To better understand these mechanisms, computational modeling has been employed, resulting in a dataset of 414 mathematical models in the BioModels database. These models vary significantly in detail and simulated processes, necessitating a robust analytical approach. Results: In this study, we apply the chemical organization theory (COT) to these models to gain insights into their dynamic behaviors. COT, which handles both ordinary and partial differential equations (ODEs and PDEs), is utilized to analyze the compartmentalized structures of these models. COT’s framework allows for the examination of persistent subsystems within these models, even when detailed kinetic parameters are unavailable. By computing and analyzing the lattice of organizations, we can compare and rank models based on their structural features and dynamic behavior. Conclusions: Our application of the COT reveals that models with compartmentalized organizations exhibit distinctive structural features that facilitate the understanding of phenomena such as periodicity in the cell cycle. This approach provides valuable insights into the dynamics of cell cycle control mechanisms, refining existing models and potentially guiding future research in this area. Full article
(This article belongs to the Section Cell Biology and Pathology)
Show Figures

Figure 1

Figure 1
<p>A biochemical reaction network illustrating the interactions and transitions between cyclin, cdc2, and their phosphorylated states in cell cycle regulation according to the paper [<a href="#B37-biomedicines-12-02334" class="html-bibr">37</a>]. The plot was obtained from the EBI Biomodels website. The proteins cdc2 (C2, phosphorylated: C2P) and cyclin (Y, phosphorylated: YP) form a heterodimer (maturation-promoting factor) P-cyclin–cdc2 (and P-cyclin–cdc2-P) that controls the major events of the cell cycle. The numbers inside the square green box denote the reaction numbers.</p>
Full article ">Figure 2
<p>Lattice of organizations of the reaction network of Tyson’s model [<a href="#B37-biomedicines-12-02334" class="html-bibr">37</a>] with reaction numbering according to the SBML model in the BioModels database [<a href="#B69-biomedicines-12-02334" class="html-bibr">69</a>]. An illustrative representation of the organizations as subnetworks of the reaction network follows in <a href="#biomedicines-12-02334-f004" class="html-fig">Figure 4</a>.</p>
Full article ">Figure 3
<p>The numerical simulation of limit cycle oscillations in Tyson’s 1991 model [<a href="#B37-biomedicines-12-02334" class="html-bibr">37</a>] illustrates the log-scaled dynamic concentrations of Cdc2 (C2), the cyclin–Cdc2 complex (CP), phosphorylated cyclin–Cdc2 (pM), cyclin (Y), and phosphorylated cyclin (YP) over a period of 90 min. This visualization highlights the regulatory feedback mechanisms that drive cell cycle progression. The initialization values for the variables C2, CP, M, PM, Y, and Yp were 0, 0, 1, 0, 0.25, 0, and 1, respectively. The reaction constants were k<sub>1</sub> = 0.015, k<sub>2</sub> = 0, k<sub>3</sub> = 200, k<sub>4</sub> = 180, k<sub>5</sub> = 0, k<sub>6</sub> = 1, k<sub>7</sub> = 0.6, k<sub>8</sub> = 1,000,000, and k<sub>9</sub> = 1000.</p>
Full article ">Figure 4
<p>(<b>Left</b>) Reaction network of Tyson’s model [<a href="#B37-biomedicines-12-02334" class="html-bibr">37</a>] overlaid with a Venn diagram depicting the various (non-empty) organizations. (<b>Right</b>) Lattice of organizations from <a href="#biomedicines-12-02334-f002" class="html-fig">Figure 2</a>, with arrows indicating the corresponding subsystems within the reaction network shown on the left.</p>
Full article ">Figure 5
<p>Venn diagram (<b>left</b>) and lattice (<b>right</b>) of organizations in Markevich’s model [<a href="#B68-biomedicines-12-02334" class="html-bibr">68</a>]. There are four non-empty organizations. From the smallest to the biggest, these are <math display="inline"><semantics> <msub> <mi>O</mi> <mn>1</mn> </msub> </semantics></math> (light gray) containing four species; <math display="inline"><semantics> <msub> <mi>O</mi> <mn>2</mn> </msub> </semantics></math>, which contains at least two compartments, one that is colored blue (left) and one that is colored orange (right) and includes all of <math display="inline"><semantics> <msub> <mi>O</mi> <mn>1</mn> </msub> </semantics></math> and a blue-colored one; and finally <math display="inline"><semantics> <msub> <mi>O</mi> <mn>3</mn> </msub> </semantics></math> (dark gray), which is the biggest one and includes the whole system.</p>
Full article ">Figure 6
<p>Histogram of cell cycle models categorized by the organisms studied. Some models overlap, particularly those addressing transitions such as S/G2 or G2/M. The majority of the models focus on the M phase, with approximately 160 dedicated to that stage.</p>
Full article ">Figure 7
<p>Reaction network complexity: Scatter plot of the number of species vs. the number of reactions of each model. As expected, there is an overall positive correlation between the two. The number of species ranges from 1 to 189, and the number of reactions from 2 to 316.</p>
Full article ">Figure 8
<p>Lattice of organizations’ complexity: (<b>a</b>) Histogram of the number of models according to their number of organizations and (<b>b</b>) scatter plot of the number of organizations vs. the number of reactions. The most frequent number of organizations per model was two. The model size with regard to the number of reactions was not strongly connected to the number of organizations. (<b>c</b>) Height vs. width scatter plot, with values of each data point given by color. To better represent the correlation, two models were removed by cutting the diagram above a width of 10: one with a width of 56 and a height of 8, and another with a width of 20 and a height of 7. Additionally, 39 models with no species and 10 very large models in their Hasse diagram were excluded from the plot. In total, the full lattice of organizations was calculated for 275 models within the pre-limited time. (<b>d</b>) Histogram showing the values of persistence of different models. (<b>a</b>) Histogram of the model frequencies according to their number of organizations; (<b>b</b>) Number of organizations vs. number of reactions; (<b>c</b>) Height vs. width scatter plot, with values of each data point represented by color; (<b>d</b>) Histogram showing the values of persistence of different models.</p>
Full article ">Figure 9
<p>Compartmentalization complexity: (<b>a</b>) Comparison of organizations with only one compartment versus those requiring more than one compartment. (<b>b</b>) Histogram showing the count of the maximum number of required compartments in an organization. (<b>a</b>) Number of models with organizations all containing only one compartment vs. models containing at least one organization requiring more than one compartment; (<b>b</b>) histogram of required compartments across models.</p>
Full article ">Figure 10
<p>Time complexity: The range of computation time (between <math display="inline"><semantics> <msup> <mn>10</mn> <mn>15</mn> </msup> </semantics></math> and <math display="inline"><semantics> <mrow> <mn>5</mn> <mo>·</mo> <msup> <mn>10</mn> <mn>16</mn> </msup> </mrow> </semantics></math> milliseconds) required for the majority of models. The simulations were performed on a machine with an Intel Core i9-9300H CPU, a base clock speed of 2.4 GHz and 16 GB of DDR4 RAM. The operating system used was Windows 11 64-bit. (<b>a</b>) Number of reactions vs. time to compute organizations; (<b>b</b>) number of species vs. time to compute organizations; (<b>c</b>) number of organizations vs. time to compute organizations (milliseconds); (<b>d</b>) distribution of the number of models and time (milliseconds) required by them to compile.</p>
Full article ">Figure 10 Cont.
<p>Time complexity: The range of computation time (between <math display="inline"><semantics> <msup> <mn>10</mn> <mn>15</mn> </msup> </semantics></math> and <math display="inline"><semantics> <mrow> <mn>5</mn> <mo>·</mo> <msup> <mn>10</mn> <mn>16</mn> </msup> </mrow> </semantics></math> milliseconds) required for the majority of models. The simulations were performed on a machine with an Intel Core i9-9300H CPU, a base clock speed of 2.4 GHz and 16 GB of DDR4 RAM. The operating system used was Windows 11 64-bit. (<b>a</b>) Number of reactions vs. time to compute organizations; (<b>b</b>) number of species vs. time to compute organizations; (<b>c</b>) number of organizations vs. time to compute organizations (milliseconds); (<b>d</b>) distribution of the number of models and time (milliseconds) required by them to compile.</p>
Full article ">
23 pages, 8735 KiB  
Article
Fossil Diesel, Soybean Biodiesel and Hydrotreated Vegetable Oil: A Numerical Analysis of Emissions Using Detailed Chemical Kinetics at Diesel Engine Like Conditions
by Leonel R. Cancino, Jessica F. Rebelo, Felipe da C. Kraus, Eduardo H. de S. Cavalcanti, Valéria S. de B. Pimentel, Decio M. Maia and Ricardo A. B. de Sá
Atmosphere 2024, 15(10), 1224; https://doi.org/10.3390/atmos15101224 - 14 Oct 2024
Viewed by 362
Abstract
Nowadays, emissions from internal combustion engines are a relevant topic of investigation, taking into account the continuous reduction of emission limits imposed by environmental regulatory agencies around the world, obviously as the result of earnest studies that have pointed out the impact on [...] Read more.
Nowadays, emissions from internal combustion engines are a relevant topic of investigation, taking into account the continuous reduction of emission limits imposed by environmental regulatory agencies around the world, obviously as the result of earnest studies that have pointed out the impact on the human health of high levels of contaminants released into the environment. Over recent years, the use of biofuels has contributed to attenuating this environmental issue; however, new problems have been raised, such as NOx emissions tend to increase as the biofuel percentage in the fuel used in engines increases. In this research, the emissions of a compression ignition internal combustion engine modeled as a variable volume reactor with homogeneous combustion were numerically investigated. To analyze the combustion process, a detailed kinetics model tailored specifically for this purpose was used. The kinetics model comprised 30,975 chemical reactions involving 691 chemical species. Mixtures of fuel surrogates were then created to represent the fuel used in the Brazilian fuel marketplace, involving (i) fossil diesel—“diesel A”, (ii) soybean diesel—“biodiesel”, and (iii) hydrotreated vegetable oil— “HVO”. Surrogate species were then selected for each of the aforementioned fuels, and blends of those surrogates were then proposed as mixture M1 (diesel A:biodiesel:HVO—90:10:0), mixture M2 (diesel A:biodiesel:HVO—85:15:0), and mixture M3 (diesel A:biodiesel:HVO—80:15:5). The species allowed in the kinetics model included all the fuel surrogates used in this research as well as the target emission species of this study: total hydrocarbons, non-methane hydrocarbons, carbon monoxide, methane, nitrogen oxides, carbon dioxide, soot, and soot precursors. When compared to experimental trends of emissions available in the literature, it was observed that, for all the proposed fuel surrogates blends, the numerical approach performed in this research was able to capture qualitative trends for engine power and the target emissions in the whole ranges of engine speeds and engine loads, despite the CO and NOx emissions at specific engine speeds and loads. Full article
(This article belongs to the Special Issue Recent Advances in Mobile Source Emissions (2nd Edition))
Show Figures

Figure 1

Figure 1
<p>Combustion modeling. (<b>a</b>) Zero-dimensional thermodynamic models, (<b>b</b>) Quasi-dimensional models, and (<b>c</b>) Computational fluid dynamics models with chemical reaction—CRFD (Adapted from [<a href="#B31-atmosphere-15-01224" class="html-bibr">31</a>], figure (<b>c</b>) from [<a href="#B32-atmosphere-15-01224" class="html-bibr">32</a>]).</p>
Full article ">Figure 2
<p>Python engine solution flowchart.</p>
Full article ">Figure 3
<p>Pressure and temperature evolution along eight engine operation cycles at 2500 rpm, fuel injected mass = 0.125 g, mixture M1 (see <a href="#atmosphere-15-01224-t004" class="html-table">Table 4</a> and <a href="#atmosphere-15-01224-t005" class="html-table">Table 5</a> for details).</p>
Full article ">Figure 4
<p>Predicted engine expansion power (per cylinder) for all the mixtures at all numerical operation conditions simulated in this work. (Fuel mass injected per cylinder, per cycle, see <a href="#atmosphere-15-01224-t005" class="html-table">Table 5</a> for details).</p>
Full article ">Figure 5
<p>Emissions: (<b>a</b>) CO, (<b>b</b>) CO<sub>2</sub>, (<b>c</b>) CH<sub>4</sub> and (<b>d</b>) NOx—Engine at 2000 rpm (Fuel mass injected per cylinder, per cycle, see <a href="#atmosphere-15-01224-t005" class="html-table">Table 5</a> for details).</p>
Full article ">Figure 6
<p>Emissions: (<b>a</b>) Soot precursors—PAH, (<b>b</b>) Soot particles with diameter: 2 nm &lt; d &lt; 10 nm, (<b>c</b>) Soot aggregates with collision diameter: 13 nm &lt; dc &lt; 250 nm—Engine at 2000 rpm (Fuel mass injected per cylinder, per cycle, see <a href="#atmosphere-15-01224-t005" class="html-table">Table 5</a> for details).</p>
Full article ">Figure 7
<p>Emissions: (<b>a</b>) Nonmethane hydrocarbons, (<b>b</b>) unburned hydrocarbons, engine at 2000 rpm (Fuel mass injected per cylinder, per cycle, see <a href="#atmosphere-15-01224-t005" class="html-table">Table 5</a> for details).</p>
Full article ">Figure 8
<p>Relative emission to M1 mixture: (<b>a</b>) Soot particles with diameter: 2 nm &lt; d &lt; 10 nm, (<b>b</b>) Total NOx, engine at 2000 rpm (Fuel mass injected per cylinder, per cycle, see <a href="#atmosphere-15-01224-t005" class="html-table">Table 5</a> for details).</p>
Full article ">Figure 9
<p>Relative emission to M1 mixture: (<b>a</b>) CO, (<b>b</b>) CO<sub>2</sub>, (<b>c</b>) CH<sub>4</sub>, (<b>d</b>) NOx, (<b>e</b>) Soot precursors—PAH, (<b>f</b>) Soot particles, (<b>g</b>) Soot aggregates, (<b>h</b>) Non-methane hydrocarbons, (<b>i</b>) Total hydrocarbons—Engine at 1000 rpm (Fuel mass injected per cylinder, per cycle, see <a href="#atmosphere-15-01224-t005" class="html-table">Table 5</a> for details).</p>
Full article ">Figure 10
<p>Relative emission to M1 mixture: (<b>a</b>) CO, (<b>b</b>) CO<sub>2</sub>, (<b>c</b>) CH<sub>4</sub>, (<b>d</b>) NOx, (<b>e</b>) PAH, (<b>f</b>) Soot particles, (<b>g</b>) Soot aggregates, (<b>h</b>) Non-methane hydrocarbons, (<b>i</b>) Total hydrocarbons—Engine at 2000 rpm (Fuel mass injected per cylinder, per cycle, see <a href="#atmosphere-15-01224-t005" class="html-table">Table 5</a> for details).</p>
Full article ">Figure 11
<p>Relative emission to M1 mixture: (<b>a</b>) CO, (<b>b</b>) CO<sub>2</sub>, (<b>c</b>) CH<sub>4</sub>, (<b>d</b>) NOx, (<b>e</b>) PAH, (<b>f</b>) Soot particles, (<b>g</b>) Soot aggregates, (<b>h</b>) Non-methane hydrocarbons, (<b>i</b>) Total hydrocarbons—Engine at 3000 rpm (Fuel mass injected per cylinder, per cycle, see <a href="#atmosphere-15-01224-t005" class="html-table">Table 5</a> for details).</p>
Full article ">
20 pages, 7980 KiB  
Article
Theoretical Investigation into Polymorphic Transformation between β-HMX and δ-HMX by Finite Temperature String
by Xiumei Jia, Zhendong Xin, Yizheng Fu and Hongji Duan
Molecules 2024, 29(20), 4819; https://doi.org/10.3390/molecules29204819 - 11 Oct 2024
Viewed by 538
Abstract
Polymorphic transformation is important in chemical industries, in particular, in those involving explosive molecular crystals. However, due to simulating challenges in the rare event method and collective variables, understanding the transformation mechanism of molecular crystals with a complex structure at the molecular level [...] Read more.
Polymorphic transformation is important in chemical industries, in particular, in those involving explosive molecular crystals. However, due to simulating challenges in the rare event method and collective variables, understanding the transformation mechanism of molecular crystals with a complex structure at the molecular level is poor. In this work, with the constructed order parameters (OPs) and K-means clustering algorithm, the potential of mean force (PMF) along the minimum free-energy path connecting β-HMX and δ-HMX was calculated by the finite temperature string method in the collective variables (SMCV), the free-energy profile and nucleation kinetics were obtained by Markovian milestoning with Voronoi tessellations, and the temperature effect on nucleation was also clarified. The barriers of transformation were affected by the finite-size effects. The configuration with the lower potential barrier in the PMF corresponded to the critical nucleus. The time and free-energy barrier of the polymorphic transformation were reduced as the temperature increased, which was explained by the pre-exponential factor and nucleation rate. Thus, the polymorphic transformation of HMX could be controlled by the temperatures, as is consistent with previous experimental results. Finally, the HMX polymorph dependency of the impact sensitivity was discussed. This work provides an effective way to reveal the polymorphic transformation of the molecular crystal with a cyclic molecular structure, and further to prepare the desired explosive by controlling the transformation temperature. Full article
(This article belongs to the Special Issue Molecular Design and Theoretical Investigation of Energetic Materials)
Show Figures

Figure 1

Figure 1
<p><span class="html-italic">β</span>-HMX crystal structure with the size of 6 × 6 × 6 (216 molecules) from the <b>b</b>-axis view (<b>a</b>) <span class="html-italic">β</span>-HMX (a–c plane) and (<b>b</b>) <span class="html-italic">β</span>-HMX (b–c plane), and the corresponding <span class="html-italic">δ</span>-HMX crystal structure in (<b>c</b>) <span class="html-italic">δ</span>-HMX (a–c plane) and (<b>d</b>) <span class="html-italic">δ</span>-HMX (b–c plane). Red, blue, gray, and white represent O, N, C, and H atoms, respectively.</p>
Full article ">Figure 2
<p>An illustration of the OP construction for HMX. The vector <b><span class="html-italic">r</span></b> joins the center of mass of the two HM molecules (<math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>r</mi> <mo stretchy="false">^</mo> </mover> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> </mrow> </semantics></math>). The direction of the axis passing through the center of the six-membered ring and perpendicular to the plane formed by the three C atoms or three N atoms on the ring is used as an approximate measure of the absolute orientation (<span class="html-italic">q<sub>i</sub></span> or <span class="html-italic">q<sub>j</sub></span> for molecule <span class="html-italic">i</span> or <span class="html-italic">j</span>). The bond orientation <math display="inline"><semantics> <mrow> <msub> <mi>ϕ</mi> <mover accent="true"> <mi>r</mi> <mo stretchy="false">^</mo> </mover> </msub> </mrow> </semantics></math> defined as the projection of <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>r</mi> <mo stretchy="false">^</mo> </mover> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> </mrow> </semantics></math> onto <span class="html-italic">q<sub>i</sub></span> or <span class="html-italic">q<sub>j</sub></span>, and the relative orientation <math display="inline"><semantics> <mrow> <msub> <mi>ϕ</mi> <mi>q</mi> </msub> </mrow> </semantics></math> that shows the rotates of <span class="html-italic">n<sub>i</sub></span> onto <span class="html-italic">n<sub>j</sub></span> (n = 1, 2, and 3).</p>
Full article ">Figure 3
<p>Convergence of collective variables during the evolution of the string. (<b>a</b>): (I), (II), and (III) orderly correspond to <math display="inline"><semantics> <mrow> <msubsup> <mi>θ</mi> <mi mathvariant="normal">C</mi> <mi>d</mi> </msubsup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msubsup> <mi>θ</mi> <mi mathvariant="normal">C</mi> <mi>b</mi> </msubsup> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <msubsup> <mi>θ</mi> <mi mathvariant="normal">C</mi> <mi>r</mi> </msubsup> </mrow> </semantics></math> OPs as the collective variables for K-means clustering sampling, respectively. (<b>b</b>): (IV), (V), (VI), and (VII) orderly correspond to <math display="inline"><semantics> <mrow> <msubsup> <mi>θ</mi> <mi mathvariant="normal">C</mi> <mrow> <mi>d</mi> <mi>b</mi> </mrow> </msubsup> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msubsup> <mi>θ</mi> <mi mathvariant="normal">C</mi> <mrow> <mi>d</mi> <mi>r</mi> </mrow> </msubsup> </mrow> </semantics></math> with K-means clustering, <math display="inline"><semantics> <mrow> <msubsup> <mi>θ</mi> <mi mathvariant="normal">C</mi> <mrow> <mi>d</mi> <mi>r</mi> </mrow> </msubsup> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msubsup> <mi>θ</mi> <mi mathvariant="normal">C</mi> <mrow> <mi>d</mi> <mi>b</mi> </mrow> </msubsup> </mrow> </semantics></math> with the average-based sampling, respectively.</p>
Full article ">Figure 4
<p>PMF as a function of the arclength along the FTS path. The initial point at arclength zero is <span class="html-italic">δ</span>-HMX, and the end point is <span class="html-italic">β</span>-HMX. (I), (II), and (III) are orderly the FTS path from <math display="inline"><semantics> <mrow> <msubsup> <mi>θ</mi> <mi mathvariant="normal">C</mi> <mrow> <mi>d</mi> <mi>b</mi> </mrow> </msubsup> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msubsup> <mi>θ</mi> <mi mathvariant="normal">C</mi> <mrow> <mi>d</mi> <mi>r</mi> </mrow> </msubsup> </mrow> </semantics></math> with K-means clustering, <math display="inline"><semantics> <mrow> <msubsup> <mi>θ</mi> <mi mathvariant="normal">C</mi> <mrow> <mi>d</mi> <mi>r</mi> </mrow> </msubsup> </mrow> </semantics></math> without K-means clustering, respectively. (<b>a</b>,<b>b</b>) mean the PMF curves corresponding to the <math display="inline"><semantics> <mrow> <msubsup> <mi>θ</mi> <mi mathvariant="normal">C</mi> <mrow> <mi>d</mi> <mi>b</mi> </mrow> </msubsup> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msubsup> <mi>θ</mi> <mi mathvariant="normal">C</mi> <mrow> <mi>d</mi> <mi>r</mi> </mrow> </msubsup> </mrow> </semantics></math> with the K-means clustering sampling, and (<b>c</b>) is the PMF curve involving <math display="inline"><semantics> <mrow> <msubsup> <mi>θ</mi> <mi mathvariant="normal">C</mi> <mrow> <mi>d</mi> <mi>r</mi> </mrow> </msubsup> </mrow> </semantics></math> from the average-based sampling.</p>
Full article ">Figure 5
<p>Changes in the local order parameters on the FTS path and times (“~” represents “approximately”); 0 ns (IA) ~2.0 ns (IB) ~4.0 ns (IC) ~5.0 ns (ID) ~6.0 ns ~8.0 ns (I) <math display="inline"><semantics> <mrow> <msubsup> <mi>θ</mi> <mi mathvariant="normal">C</mi> <mrow> <mi>d</mi> <mi>b</mi> </mrow> </msubsup> </mrow> </semantics></math> with K-means clustering (IIA) ~2.0 ns (IIB) ~4.0 ns (IIC) ~6.0 ns ~8.0 ns (II) <math display="inline"><semantics> <mrow> <msubsup> <mi>θ</mi> <mi mathvariant="normal">C</mi> <mrow> <mi>d</mi> <mi>r</mi> </mrow> </msubsup> </mrow> </semantics></math> with K-means clustering (IIIA) ~2.0 ns (IIIB) ~4.0 ns (IIIC) ~6.0 ns (IIID) ~8.0 ns (III) <math display="inline"><semantics> <mrow> <msubsup> <mi>θ</mi> <mi mathvariant="normal">C</mi> <mrow> <mi>d</mi> <mi>r</mi> </mrow> </msubsup> </mrow> </semantics></math> without K-means clustering.</p>
Full article ">Figure 5 Cont.
<p>Changes in the local order parameters on the FTS path and times (“~” represents “approximately”); 0 ns (IA) ~2.0 ns (IB) ~4.0 ns (IC) ~5.0 ns (ID) ~6.0 ns ~8.0 ns (I) <math display="inline"><semantics> <mrow> <msubsup> <mi>θ</mi> <mi mathvariant="normal">C</mi> <mrow> <mi>d</mi> <mi>b</mi> </mrow> </msubsup> </mrow> </semantics></math> with K-means clustering (IIA) ~2.0 ns (IIB) ~4.0 ns (IIC) ~6.0 ns ~8.0 ns (II) <math display="inline"><semantics> <mrow> <msubsup> <mi>θ</mi> <mi mathvariant="normal">C</mi> <mrow> <mi>d</mi> <mi>r</mi> </mrow> </msubsup> </mrow> </semantics></math> with K-means clustering (IIIA) ~2.0 ns (IIIB) ~4.0 ns (IIIC) ~6.0 ns (IIID) ~8.0 ns (III) <math display="inline"><semantics> <mrow> <msubsup> <mi>θ</mi> <mi mathvariant="normal">C</mi> <mrow> <mi>d</mi> <mi>r</mi> </mrow> </msubsup> </mrow> </semantics></math> without K-means clustering.</p>
Full article ">Figure 6
<p>Free energy for the nucleation of polymorphic transformation from the Markovian milestoning with Voronoi tessellations. The left and right sides of the curve correspond to <span class="html-italic">δ</span>-HMX and <span class="html-italic">β</span>-HMX crystals. (<b>a</b>,<b>b</b>) are obtained from the string by K-means clustering sampling with <math display="inline"><semantics> <mrow> <msubsup> <mi>θ</mi> <mi mathvariant="normal">C</mi> <mrow> <mi>d</mi> <mi>b</mi> </mrow> </msubsup> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msubsup> <mi>θ</mi> <mi mathvariant="normal">C</mi> <mrow> <mi>d</mi> <mi>r</mi> </mrow> </msubsup> </mrow> </semantics></math>, respectively.</p>
Full article ">Figure 7
<p>Mean first passage time to <span class="html-italic">β</span>-HMX.</p>
Full article ">Figure 8
<p>Changes in the local order parameters on the FTS path and the times for crystallization involving <math display="inline"><semantics> <mrow> <msubsup> <mi>θ</mi> <mi mathvariant="normal">C</mi> <mrow> <mi>d</mi> <mi>r</mi> </mrow> </msubsup> </mrow> </semantics></math> with K-means clustering at different temperatures (“~” represents “approximately”). (IIA) ~2.0 ns (IIB) ~4.0 ns (IIC) ~5.0 ns ~6.0 ns (<b>a-1</b>–<b>a-4</b>) at 510 K; (IIA) ~2.0 ns (IIB) ~6.0 ns (IIC) ~8.0 ns ~12.0 ns (<b>b-1</b>–<b>b-4</b>) at 450 K; (IIA) ~2.0 ns (IIB) ~8.0 ns (IIC) ~10.0 ns ~13.0 ns (<b>c-1</b>–<b>c-4</b>) at 420 K.</p>
Full article ">Figure 9
<p>Free energy obtained by Markovian milestoning with Voronoi tessellations at different temperatures, with K-means clustering sampling. (<b>a</b>) 510 K, (<b>b</b>) 450 K, (<b>c</b>) 420 K.</p>
Full article ">Figure 10
<p>Mean first passage time to <span class="html-italic">β</span>-HMX at different temperatures. (<b>a</b>) 510 K, (<b>b</b>) 450 K, (<b>c</b>) 420 K.</p>
Full article ">
17 pages, 4576 KiB  
Article
Mechanism of Enhanced Fluoride Adsorption Using Amino-Functionalized Aluminum-Based Metal–Organic Frameworks
by Yiting Luo, Zhao Liu, Mingqiang Ye, Yihui Zhou, Rongkui Su, Shunhong Huang, Yonghua Chen and Xiangrong Dai
Water 2024, 16(20), 2889; https://doi.org/10.3390/w16202889 - 11 Oct 2024
Viewed by 463
Abstract
Due to the increasing fluoride concentrations in water bodies, significant environmental concerns have arisen. This study focuses on aluminum-based materials with a high affinity for fluorine, specifically enhancing metal–organic frameworks (MOFs) with amino groups to improve their adsorption and defluorination performance. We systematically [...] Read more.
Due to the increasing fluoride concentrations in water bodies, significant environmental concerns have arisen. This study focuses on aluminum-based materials with a high affinity for fluorine, specifically enhancing metal–organic frameworks (MOFs) with amino groups to improve their adsorption and defluorination performance. We systematically investigate the factors influencing and mechanisms governing the adsorption and defluorination behavior of amino-functionalized aluminum-based MOF materials in aqueous environments. An SEM, XRD, and FT-IR characterization confirms the successful preparation of NH2-MIL-101 (Al). In a 10 mg/L fluoride ion solution at pH 7.0, fluoride ion removal efficiency increases with the dosage of NH2-MIL-101 (Al), although the marginal improvement decreases beyond 0.015 g/L. Under identical conditions, the fluoride adsorption capacity of NH2-MIL-101 (Al) is seven times greater than that of NH2-MIL-101 (Fe). NH2-MIL-101 (Al) demonstrates effective fluoride ion adsorption across a broad pH range, with superior fluoride uptake in acidic conditions. At a fluoride ion concentration of 7 mg/L, with 0.015 g of NH2-MIL-101 (Al) at pH 3.0, adsorption equilibrium is achieved within 60 min, with a capacity of 31.2 mg/g. An analysis using adsorption isotherm models reveals that the fluoride ion adsorption on NH2-MIL-101 (Al) follows a monolayer adsorption model, while kinetic studies indicate that the predominant adsorption mechanism is chemical adsorption. This research provides a scientific basis for the advanced treatment of fluoride-containing wastewater, offering significant theoretical and practical contributions. Full article
Show Figures

Figure 1

Figure 1
<p>XRD of NH<sub>2</sub>-MIL-101 (Al).</p>
Full article ">Figure 2
<p>FT-IR Spectrum of NH<sub>2</sub>-MIL-101 (Al).</p>
Full article ">Figure 3
<p>SEM image of NH<sub>2</sub>-MIL-101 (Al) (<b>Left</b>: 100 nm, <b>Right</b>: 5 µm).</p>
Full article ">Figure 4
<p>N2 adsorption–desorption isotherm (<b>a</b>) and pore size analysis (<b>b</b>) of NH<sub>2</sub>-MIL-101(Fe).</p>
Full article ">Figure 5
<p>Effect of material dosage on adsorption performance.</p>
Full article ">Figure 6
<p>Effect of initial fluoride concentration on adsorption performance.</p>
Full article ">Figure 7
<p>Effect of solution pH on adsorption performance.</p>
Full article ">Figure 8
<p>Adsorption equilibrium curve of NH<sub>2</sub>-MIL-101 (Al).</p>
Full article ">Figure 9
<p>Pseudo-first-order kinetic model.</p>
Full article ">Figure 10
<p>Pseudo-second-order kinetic model.</p>
Full article ">Figure 11
<p>Langmuir model.</p>
Full article ">Figure 12
<p>Freundlich model.</p>
Full article ">
17 pages, 5655 KiB  
Article
Garlic Peel-Based Biochar Prepared under Weak Carbonation Conditions for Efficient Removal of Methylene Blue from Wastewater
by Tao-Tao Shi, Bi Yang, Wei-Guo Hu, Guan-Jin Gao, Xin-Yu Jiang and Jin-Gang Yu
Molecules 2024, 29(19), 4772; https://doi.org/10.3390/molecules29194772 - 9 Oct 2024
Viewed by 347
Abstract
Background: Due to it containing cellulose, hemicellulose, and lignin with abundant specific functional groups which could interact with organic dyes, garlic peel (GP) might be used as an efficient biosorbent. The aim of this study is to evaluate the adsorption performances of GP-based [...] Read more.
Background: Due to it containing cellulose, hemicellulose, and lignin with abundant specific functional groups which could interact with organic dyes, garlic peel (GP) might be used as an efficient biosorbent. The aim of this study is to evaluate the adsorption performances of GP-based bio-adsorbents and obtain optimum preparation conditions. Methods: GP-based bio-adsorbents were prepared by thermal pyrolysis under different temperatures (150–400 °C). The morphologies, chemical states, and surface functional groups of the adsorbents were analyzed by X-ray photoelectron spectroscopy (XPS), Fourier transform infrared spectroscopy (FT-IR), scanning electron microscopy (SEM), and thermogravimetric analysis (TGA). Batch experiments were conducted to investigate the adsorption of methylene blue (MB) under various conditions, including contact time, contact temperature, initial dye concentration, and initial pH value. The equilibrium adsorption data were fitted to different kinetic and isothermal models, and the adsorption thermodynamics were also calculated. Significant Findings: The physicochemical properties of the GP-based bio-adsorbents were primarily dominated by the pyrolysis temperature, because their morphologies and surface functional groups of GP-based bio-adsorbents significantly varied with the changes in pyrolysis temperature. The adsorption capacity of GP materials for MB decreased as the pyrolysis temperature increased. At an initial concentration of 50.00 mg L−1, GP150 possessed a higher adsorption capacity of 167.74 mg g−1 toward MB. The possible adsorbate–adsorbent interactions, including electrostatic attraction, hydrogen bonding, and π-π stacking, were recognized. After 10 consecutive adsorption–desorption cycles, GP150 maintained a high removal rate (88%) for MB, demonstrating its excellent adsorption performance, good reusability, and potential application in the treatment of MB-contaminated water. Full article
Show Figures

Figure 1

Figure 1
<p>SEM images of GP150 material pre- (<b>a</b>) and post-adsorption (<b>b</b>) of MB.</p>
Full article ">Figure 2
<p>EDS and elemental mapping images of GP150: (<b>a</b>) SEM image of the scanned area; (<b>b</b>) C; (<b>c</b>) O; (<b>d</b>) N; (<b>e</b>) EDS of GP150; EDS and elemental mapping images of GP1500-MB: (<b>f</b>) SEM image of the scanned area; (<b>g</b>) C; (<b>h</b>) O; (<b>i</b>) N; (<b>j</b>) EDS.</p>
Full article ">Figure 2 Cont.
<p>EDS and elemental mapping images of GP150: (<b>a</b>) SEM image of the scanned area; (<b>b</b>) C; (<b>c</b>) O; (<b>d</b>) N; (<b>e</b>) EDS of GP150; EDS and elemental mapping images of GP1500-MB: (<b>f</b>) SEM image of the scanned area; (<b>g</b>) C; (<b>h</b>) O; (<b>i</b>) N; (<b>j</b>) EDS.</p>
Full article ">Figure 3
<p>FT-IR spectra: (<b>a</b>) GP materials obtained by vacuum pyrolysis at different temperatures; (<b>b</b>) MB and GP150 pre- and post-adsorption of MB.</p>
Full article ">Figure 4
<p>XPS spectra of GP150: (<b>a</b>) Survey spectra; XPS-peak-differentiation-imitating analyses of C 1s (<b>b</b>) and O 1s (<b>c</b>). XPS spectra of GP150-MB: (<b>d</b>) Survey spectra; XPS-peak-differentiation-imitating analyses of C 1s (<b>e</b>) and O 1s (<b>f</b>).</p>
Full article ">Figure 4 Cont.
<p>XPS spectra of GP150: (<b>a</b>) Survey spectra; XPS-peak-differentiation-imitating analyses of C 1s (<b>b</b>) and O 1s (<b>c</b>). XPS spectra of GP150-MB: (<b>d</b>) Survey spectra; XPS-peak-differentiation-imitating analyses of C 1s (<b>e</b>) and O 1s (<b>f</b>).</p>
Full article ">Figure 5
<p>(<b>a</b>) N<sub>2</sub> adsorption/desorption isotherms of GP150 material; (<b>b</b>) pore volume distribution of the GP150 material by the Barrett-Joyner-Halenda (BJH) method.</p>
Full article ">Figure 6
<p>(<b>a</b>) Adsorption capacities of the GP200 material toward different dyes and phenols in aqueous solutions (<span class="html-italic">C</span><sub>0</sub> = 50 mg L<sup>−1</sup>, adsorbent dosage = 5.0 mg, contact time = 120 min, T = 25 °C, pH = at the natural pH of aqueous adsorbate solutions); (<b>b</b>) adsorption of MB by GP150, GP200, GP250, GP300, GP350, and GP400 (<span class="html-italic">C</span><sub>0</sub> = 50 mg L<sup>−1</sup>, adsorbent dosage = 5.0 mg, contact time = 120 min, T = 25 °C, pH = the natural pH of the aqueous adsorbate solution).</p>
Full article ">Figure 7
<p>Effect of contact time on the removal of MB dye by the GP150 material (<span class="html-italic">C</span><sub>0</sub> = 50.0 mg L<sup>−1</sup>, T = 25 °C, pH = at the natural pH values of aqueous MB solutions).</p>
Full article ">Figure 8
<p>Adsorption properties of the GP150 material for MB: (<b>a</b>) effect of contact temperature (adsorbent dosage = 5.0 mg, <span class="html-italic">t</span> = 60 min, pH = the natural pH values of the dye stuffs); (<b>b</b>) effect of initial MB concentration (adsorbent dosage = 5.0 mg, <span class="html-italic">t</span> = 60 min, pH = the natural pH values of the dye stuffs).</p>
Full article ">Figure 9
<p>(<b>a</b>) Zeta potential of GP150 in aqueous solution at different pH values; (<b>b</b>) effect of initial solution pH value (<span class="html-italic">C</span><sub>0</sub> = 50.0 mg L<sup>−1</sup>, <span class="html-italic">t</span> = 60 min, T = 25 °C).</p>
Full article ">Figure 10
<p>(<b>a</b>) The adsorption of MB from actual water samples by GP150 (<span class="html-italic">C</span><sub>0</sub> = 50.0 mg L<sup>−1</sup>, <span class="html-italic">t</span> = 60 min, T = 25 °C, pH = the natural pH values of the dye stuffs); (<b>b</b>) reusability of GP150 for removal of MB in successive 10 adsorption–desorption cycles (C<sub>0</sub> = 20 mg L<sup>−1</sup>, adsorbent dosage = 50.0 mg, t = 60 min, T = 25 °C, pH = the natural pH values of the dye stuffs).</p>
Full article ">Figure 11
<p>A proposed mechanism of MB adsorption by the GP150 material.</p>
Full article ">
43 pages, 8271 KiB  
Review
Valorization of Eggshell as Renewable Materials for Sustainable Biocomposite Adsorbents—An Overview
by Bolanle M. Babalola and Lee D. Wilson
J. Compos. Sci. 2024, 8(10), 414; https://doi.org/10.3390/jcs8100414 - 8 Oct 2024
Viewed by 587
Abstract
The production and buildup of eggshell waste represents a challenge and an opportunity. The challenge is that uncontrolled disposal of generated eggshell waste relates to a sustainability concern for the environment. The opportunity relates to utilization of this biomass resource via recycling for [...] Read more.
The production and buildup of eggshell waste represents a challenge and an opportunity. The challenge is that uncontrolled disposal of generated eggshell waste relates to a sustainability concern for the environment. The opportunity relates to utilization of this biomass resource via recycling for waste valorization, cleaner production, and development of a circular economy. This review explores the development of eggshell powder (ESP) from eggshell waste and a coverage of various ESP composite sorbents with an emphasis on their potential utility as adsorbent materials for model pollutants in solid–liquid systems. An overview of literature since 2014 outlines the development of eggshell powder (ESP) and ESP composite adsorbents for solid–liquid adsorption processes. The isolation and treatment of ESP in its pristine or modified forms by various thermal or chemical treatments, along with the preparation of ESP biocomposites is described. An overview of the physico-chemical characterization of ESP and its biocomposites include an assessment of the adsorption properties with various model pollutants (cations, anions, and organic dyes). A coverage of equilibrium and kinetic adsorption isotherm models is provided, along with relevant thermodynamic parameters that govern the adsorption process for ESP-based adsorbents. This review reveals that ESP biocomposite adsorbents represent an emerging class of sustainable materials with tailored properties via modular synthetic strategies. This review will serve to encourage the recycling and utilization of eggshell biomass waste and its valorization as potential adsorbent systems. The impact of such ESP biosorbents cover a diverse range of adsorption-based applications from environmental remediation to slow-release fertilizer carrier systems in agricultural production. Full article
(This article belongs to the Special Issue Sustainable Biocomposites, Volume II)
Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Global egg production for two decades, where production (10<sup>6</sup> metric tons) covers a four-decade period. Redrawn with permission from [<a href="#B15-jcs-08-00414" class="html-bibr">15</a>].</p>
Full article ">Figure 2
<p>The overall structure of an egg and the eggshell. Copied with permission [<a href="#B26-jcs-08-00414" class="html-bibr">26</a>].</p>
Full article ">Figure 3
<p>Steps involved in the preparation of ESP.</p>
Full article ">Figure 4
<p>General flowchart of the preparatory steps for making eggshell biocomposite materials that contain various additives (1 to 8), as follows: (1) anthill clay, (2) multi-walled carbon nanotubes (MWCNTs), (3) sodium alginate, (4) titanium dioxide, (5) strontium ferrite, (6) eggshell powder (ESP), (7) sodium dodecyl sulfate (SDS), and (8) chitosan/acetic acid.</p>
Full article ">Figure 5
<p>Typical characterization results of ESP. (<b>A</b>) SEM: (a) ESP; (b) CES; (<b>B</b>) XRD; (<b>C</b>) XPS; (<b>D</b>) IR copied with permission [<a href="#B7-jcs-08-00414" class="html-bibr">7</a>,<a href="#B58-jcs-08-00414" class="html-bibr">58</a>,<a href="#B59-jcs-08-00414" class="html-bibr">59</a>].</p>
Full article ">Figure 6
<p>Simplified illustration of an adsorption experiment. Adapted and redrawn with permission [<a href="#B74-jcs-08-00414" class="html-bibr">74</a>].</p>
Full article ">Figure 7
<p>Adsorption of an adsorbate in the liquid phase onto a solid adsorbent at the solid–liquid interface. The circles depict the adsorbate particles while the dashed line represent the imaginary interface boundary. Copied and modified with permission [<a href="#B77-jcs-08-00414" class="html-bibr">77</a>].</p>
Full article ">Figure 8
<p>Contributing factors for the adsorption mechanism of pollutants onto eggshell particles. Copied with permission [<a href="#B146-jcs-08-00414" class="html-bibr">146</a>].</p>
Full article ">Figure 9
<p>Application of eggshell in various industries. Copied with permission [<a href="#B74-jcs-08-00414" class="html-bibr">74</a>].</p>
Full article ">Figure 10
<p>Application of eggshell waste in catalysis.</p>
Full article ">Figure 11
<p>Application of ES as a photocatalyst in water treatment. Copied and redrawn with permission [<a href="#B59-jcs-08-00414" class="html-bibr">59</a>].</p>
Full article ">Figure 12
<p>Application of eggshell as a slow-release fertilizer system. Adapted with permission [<a href="#B195-jcs-08-00414" class="html-bibr">195</a>].</p>
Full article ">Figure 13
<p>(<b>a</b>) Water holding capacity and (<b>b</b>) water retention capacity of soil with and without ES and ES-SRF. Copied and modified with permission [<a href="#B195-jcs-08-00414" class="html-bibr">195</a>].</p>
Full article ">Figure 14
<p>The use of granular ternary agro-waste adsorbent for orthophosphate uptake at pH 4.5 and 8.5. Copied with permission [<a href="#B16-jcs-08-00414" class="html-bibr">16</a>].</p>
Full article ">Figure 15
<p>Application of eggshell in treatment of water containing metal-ion species. Copied with permission [<a href="#B197-jcs-08-00414" class="html-bibr">197</a>].</p>
Full article ">
19 pages, 582 KiB  
Article
Two-Stage Global Biomass Pyrolysis Model for Combustion Applications: Predicting Product Composition with a Focus on Kinetics, Energy, and Mass Balances Consistency
by Germán Navarrete Cereijo, Pedro Galione Klot and Pedro Curto-Risso
Energies 2024, 17(19), 4982; https://doi.org/10.3390/en17194982 - 5 Oct 2024
Viewed by 602
Abstract
This work presents a comprehensive model for lignocellulosic biomass pyrolysis, addressing kinetics, energy balances, and gas product composition with the aim of its application in wood combustion. The model consists of a two-stage global mechanism in which biomass initially reacts into tar, char, [...] Read more.
This work presents a comprehensive model for lignocellulosic biomass pyrolysis, addressing kinetics, energy balances, and gas product composition with the aim of its application in wood combustion. The model consists of a two-stage global mechanism in which biomass initially reacts into tar, char, and light gases (non-condensable gases), which is followed by tar reacting into light gases and char. Experimental data from the literature are employed for determining Arrhenius kinetic parameters and key energy parameters, like tar and char heating values and the specific enthalpy of primary and secondary reactions. A methodology is introduced to derive correlations, allowing the model’s application to diverse biomass types. This work introduces several novel approaches. Firstly, a pyrolysis model that determines the composition of light gases by solving mass, species, and energy balances is developed, limiting the use of correlations from the literature only for tar and char elemental composition. The mass rate of light gases, tar, and char being produced is also determined. Secondly, kinetic parameters for primary and secondary reactions are determined following a Shafizadeh and Chin scheme but with a modified Arrhenius form dependent on Tn, significantly enhancing the accuracy of product composition prediction. Additionally, correlations for the enthalpies of reactions, both primary and secondary, are determined as a function of pyrolysis temperature. Primary reactions exhibit an overall endothermic behavior, while secondary reactions exhibit an overall exothermic behavior. Finally, the model is validated using cases reported in the literature, and results for light gases composition are presented. Full article
(This article belongs to the Special Issue Advances in Fuels and Combustion)
Show Figures

Figure 1

Figure 1
<p>Scheme of reactions in the pyrolysis process.</p>
Full article ">Figure 2
<p>Higher heating value of tar and char from literature along with their respective linear trends [<a href="#B32-energies-17-04982" class="html-bibr">32</a>,<a href="#B35-energies-17-04982" class="html-bibr">35</a>,<a href="#B39-energies-17-04982" class="html-bibr">39</a>,<a href="#B40-energies-17-04982" class="html-bibr">40</a>,<a href="#B41-energies-17-04982" class="html-bibr">41</a>,<a href="#B42-energies-17-04982" class="html-bibr">42</a>,<a href="#B43-energies-17-04982" class="html-bibr">43</a>,<a href="#B44-energies-17-04982" class="html-bibr">44</a>].</p>
Full article ">Figure 3
<p>Arrhenius parameters compensation effect curve (KCE): selected kinetic parameters based on the modified Arrhenius equation compared with literature data [<a href="#B6-energies-17-04982" class="html-bibr">6</a>,<a href="#B11-energies-17-04982" class="html-bibr">11</a>,<a href="#B12-energies-17-04982" class="html-bibr">12</a>,<a href="#B17-energies-17-04982" class="html-bibr">17</a>,<a href="#B19-energies-17-04982" class="html-bibr">19</a>,<a href="#B20-energies-17-04982" class="html-bibr">20</a>,<a href="#B21-energies-17-04982" class="html-bibr">21</a>,<a href="#B22-energies-17-04982" class="html-bibr">22</a>,<a href="#B23-energies-17-04982" class="html-bibr">23</a>,<a href="#B24-energies-17-04982" class="html-bibr">24</a>,<a href="#B25-energies-17-04982" class="html-bibr">25</a>,<a href="#B26-energies-17-04982" class="html-bibr">26</a>].</p>
Full article ">Figure 4
<p>Final mass fractions of light gases, condensables, and char: comparison of results with selected kinetic parameters with literature (lit.) data considering primary and secondary reactions [<a href="#B12-energies-17-04982" class="html-bibr">12</a>,<a href="#B21-energies-17-04982" class="html-bibr">21</a>,<a href="#B22-energies-17-04982" class="html-bibr">22</a>,<a href="#B23-energies-17-04982" class="html-bibr">23</a>,<a href="#B24-energies-17-04982" class="html-bibr">24</a>,<a href="#B30-energies-17-04982" class="html-bibr">30</a>]. Points from the experimental data where secondary reactions are limited are excluded.</p>
Full article ">Figure 5
<p>Specific enthalpy, expressed per kilogram of biomass consumed, for the adjustment curves of primary reactions for eucalyptus.</p>
Full article ">Figure 6
<p>Specific enthalpy, expressed per kilogram of tar consumed, for the adjustment curves of secondary reactions for eucalyptus.</p>
Full article ">Figure 7
<p>Comparison of the mass fraction adjustment with respect to TGA for different biomass and heating rates. (<b>a</b>) Eucalyptus at 10 K/min [<a href="#B61-energies-17-04982" class="html-bibr">61</a>]. (<b>b</b>) Beech at 5 K/min, 25 K/min and 50 K/min [<a href="#B55-energies-17-04982" class="html-bibr">55</a>]. (<b>c</b>) Poplar at 2 K/min and 15 K/min [<a href="#B56-energies-17-04982" class="html-bibr">56</a>].</p>
Full article ">Figure 8
<p>Mass fraction of pyrolysis products inducing light gases composition [<a href="#B21-energies-17-04982" class="html-bibr">21</a>,<a href="#B22-energies-17-04982" class="html-bibr">22</a>,<a href="#B23-energies-17-04982" class="html-bibr">23</a>,<a href="#B30-energies-17-04982" class="html-bibr">30</a>,<a href="#B31-energies-17-04982" class="html-bibr">31</a>].</p>
Full article ">
22 pages, 5740 KiB  
Article
Development of Scaffolds with Chitosan Magnetically Activated with Cobalt Nanoferrite: A Study on Physical-Chemical, Mechanical, Cytotoxic and Antimicrobial Behavior
by Danyelle Garcia Guedes, Gabryella Garcia Guedes, Jessé de Oliveira da Silva, Adriano Lima da Silva, Carlos Bruno Barreto Luna, Bolívar Ponciano Goulart de Lima Damasceno and Ana Cristina Figueiredo de Melo Costa
Pharmaceuticals 2024, 17(10), 1332; https://doi.org/10.3390/ph17101332 - 5 Oct 2024
Viewed by 551
Abstract
Background/Objectives: This study investigates the development of 3D chitosan-x-cobalt ferrite scaffolds (x = 5, 7.5, and 10 wt%) with interconnected porosity for potential biomedical applications. The objective was to evaluate the effects of magnetic particle incorporation on the scaffolds’ structural, mechanical, magnetic, [...] Read more.
Background/Objectives: This study investigates the development of 3D chitosan-x-cobalt ferrite scaffolds (x = 5, 7.5, and 10 wt%) with interconnected porosity for potential biomedical applications. The objective was to evaluate the effects of magnetic particle incorporation on the scaffolds’ structural, mechanical, magnetic, and biological properties, specifically focusing on their biocompatibility and antimicrobial performance. Methods: Scaffolds were synthesized using freeze-drying, while cobalt ferrite nanoparticles were produced via a pilot-scale combustion reaction. The scaffolds were characterized for their physical and chemical properties, including porosity, swelling, and mechanical strength. Hydrophilicity was assessed through contact angle measurements. Antimicrobial efficacy was evaluated using time kill kinetics and agar diffusion assays, and biocompatibility was confirmed through cytotoxicity tests. Results: The incorporation of cobalt ferrite increased magnetic responsiveness, altered porosity profiles, and influenced swelling, biodegradation, and compressive strength, with a maximum value of 87 kPa at 7.5 wt% ferrite content. The scaffolds maintained non-toxicity and demonstrated bactericidal activity. The optimal concentration for achieving a balance between structural integrity and biological performance was found at 7.5 wt% cobalt ferrite. Conclusions: These findings suggest that magnetic chitosan-cobalt ferrite scaffolds possess significant potential for use in biomedical applications, including tissue regeneration and advanced healing therapies. The incorporation of magnetic properties enhances both the structural and biological functionalities, presenting promising opportunities for innovative therapeutic approaches in reconstructive procedures. Full article
(This article belongs to the Special Issue Biodegradable Polymeric Nanosystems for Drug Delivery)
Show Figures

Figure 1

Figure 1
<p>(<b>a</b>) FT-IR spectra and (<b>b</b>) XRD patterns of chitosan-CS, cobalt ferrite-CFO, reference specimen-S1, and magnetic scaffolds S2, S3, and S4.</p>
Full article ">Figure 2
<p>(<b>a</b>) SEM micrographs of a reference specimen, (<b>b</b>) S2, (<b>c</b>) S3, and (<b>d</b>) S4 magnetic scaffolds.</p>
Full article ">Figure 3
<p>(<b>a</b>) EDS spectrum and (<b>b</b>) mapping images of three scaffolds showing the different distribution patterns of Fe and Co on the surface.</p>
Full article ">Figure 4
<p>(<b>a</b>) Magnetization hysteresis loops and (<b>b</b>) digital photos of magnets interacting with various scaffolds.</p>
Full article ">Figure 5
<p>Swelling behavior of scaffolds in PBS. Error bars indicate mean ± SD (<span class="html-italic">n</span> = 5).</p>
Full article ">Figure 6
<p>Contact angle of the S1, S2, S3, and S4 scaffolds. Error bars indicate mean ± SD (<span class="html-italic">n</span> = 5).</p>
Full article ">Figure 7
<p>Mechanical strength values for scaffolds. Error bars indicate mean ± SD (<span class="html-italic">n</span> = 5).</p>
Full article ">Figure 8
<p>Degradation behavior of scaffolds after immersion in PBS solution, after 14 days and 21 days. Error bars indicate mean ± SD (<span class="html-italic">n</span> = 5).</p>
Full article ">Figure 9
<p>Inverted digital microscope images at 100× magnification of the L929 cell line of the areas of discoloration around the agar diffusion test scaffolds: (<b>a</b>) positive control (toxic latex), (<b>b</b>) negative control (quantitative filter paper), (<b>c</b>) S2, and (<b>d</b>) S4.</p>
Full article ">Figure 10
<p>Antimicrobial activity: (<b>a</b>) zone of inhibition of the scaffolds against <span class="html-italic">S. aureus</span>, <span class="html-italic">E. coli</span>, <span class="html-italic">P. aeruginosa</span>, <span class="html-italic">C. albicans</span>, and <span class="html-italic">C. glabrata</span>. The error bars indicate the mean ± SD (<span class="html-italic">n</span> = 3), representing the significant differences between S1, S2, S3, S4, and the control. (<b>b</b>) Cefalozin (positive control) and S3 scaffold inhibition zone images of representative disk diffusion test plates.</p>
Full article ">Figure 11
<p>Time kill curve. (<b>a</b>) Scatterplot with straight lines and markers of <span class="html-italic">Staphylococcus aureus</span> inhibition test results for S1 and S3. (<b>b</b>) S1 and S3 zones of bacterial growth inhibition images.</p>
Full article ">Figure 12
<p>Cobalt ferrite combustion reaction on a pilot scale.</p>
Full article ">Figure 13
<p>Scaffold fabrication process.</p>
Full article ">
15 pages, 2463 KiB  
Article
Physical–Chemical and Thermal Properties of Clays from Porto Santo Island, Portugal
by André Valente, Paula C. S. Carvalho and Fernando Rocha
Appl. Sci. 2024, 14(19), 8962; https://doi.org/10.3390/app14198962 - 5 Oct 2024
Viewed by 478
Abstract
The use of clays for thermal treatments and cosmetic purposes continues to be a worldwide practice, whether through the preservation of native cultural traditions, pharmaceutical formulations or integrative health and well-being practices. Special clays, such as bentonites, are very common for healing applications [...] Read more.
The use of clays for thermal treatments and cosmetic purposes continues to be a worldwide practice, whether through the preservation of native cultural traditions, pharmaceutical formulations or integrative health and well-being practices. Special clays, such as bentonites, are very common for healing applications due to their high cation exchange capacity (CEC), high specific surface area (SSA) and alkaline pH values and, therefore, are used in multiple therapeutic and dermocosmetic treatments. Numerous bentonitic deposits occur on Porto Santo Island with different chemical weathering degrees. This research evaluates which residual soils have the most suitable characteristics for pelotherapy. The texture of residual soils varies from silt loam to loamy sand and SSA between 39 and 90 m2/g. The pH is alkaline (8.7 to 9.6), electrical conductivity ranges from 242 to 972 µS/cm, and CEC from 50.4 to 86.8 µS/cm. The residual soils have a siliciclastic composition (41.36 to 54.02% SiO2), between 12.52 and 17.65% Al2O3 and between 52 and 82% smectite content, which are montmorillonite and nontronite. Specific heat capacity (0.5–0.9 J/g°C) and cooling kinetics (14.5–19 min) show that one residual soil has the potential to be suitable for pelotherapy according to the literature. Moreover, the residual soils have As, Cd, Co, Cr, Hg, Mn, Ni, Pb, Sb and V concentrations higher than the limits of guidelines for cosmetics and pharmaceutical products. Full article
(This article belongs to the Section Earth Sciences)
Show Figures

Figure 1

Figure 1
<p>The geographic settings of Porto Santo Island and the Madeira archipelago (<b>a</b>,<b>b</b>) and the geological setting of Porto Santo Island with the sampling distribution (<b>c</b>).</p>
Full article ">Figure 2
<p>SEM images (ampliation 150× and resolution 15 keV) of Sample 14 (<b>a</b>), with different particles sizes and shapes, and Sample 36A (<b>b</b>), with a homogeneous morphology.</p>
Full article ">Figure 3
<p>XRD graphs of samples: (<b>a</b>) 14A and (<b>b</b>) 36A.</p>
Full article ">Figure 4
<p>Photography of a fine particle (&lt;2 μm) of montmorillonite (ampliation 150× and resolution 15 keV).</p>
Full article ">
23 pages, 6808 KiB  
Article
Characterization and Hemocompatibility of α, β, and γ Cyclodextrin-Modified Magnetic Nano-Adsorbents
by Mehdi Ghaffari Sharaf, Shuhui Li, Elyn M. Rowe, Dana V. Devine and Larry D. Unsworth
Int. J. Mol. Sci. 2024, 25(19), 10710; https://doi.org/10.3390/ijms251910710 - 4 Oct 2024
Viewed by 569
Abstract
Kidney dysfunction leads to the retention of metabolites within the blood that are not effectively cleared with conventional hemodialysis. Magnetic nanoparticle (MNP)-based absorbents have inherent properties that make them amenable to capturing toxins in the blood, notably a large surface area that can [...] Read more.
Kidney dysfunction leads to the retention of metabolites within the blood that are not effectively cleared with conventional hemodialysis. Magnetic nanoparticle (MNP)-based absorbents have inherent properties that make them amenable to capturing toxins in the blood, notably a large surface area that can be chemically modified to enhance toxin capture and the ability to be easily collected from the blood using an external magnetic field. Cyclodextrins (CDs) present a chemical structure that facilitates the binding of small molecules. However, the hemocompatibility of MNPs modified with films composed of different native types of CDs (α, β, or γ) has not yet been investigated, which is information crucial to the potential clinical application of MNPs to supplement hemodialysis. To this end, films of α-, β-, or γ-CDs were formed on MNPs and characterized. The impact of these films on the adsorbed protein structure, composition of key adsorbed proteins, and clotting kinetics were evaluated. It was found that modified MNPs did not significantly affect the secondary structure of some proteins (albumin, lysozyme, α-lactalbumin). The adsorbed proteome from platelet-poor human plasma was evaluated as a function of film properties. Compared to non-modified nanoparticles, CD-modified MNPs exhibited a significant decrease in the adsorbed protein per surface area of MNPs. The immunoblot results showed variations in the adsorption levels of C3, fibrinogen, antithrombin, Factor XI, and plasminogen across CD-modified MNPs. The hemocompatibility experiments showed that CD-modified MNPs are compatible with human whole blood, with no significant impact on platelet activation, hemolysis, or hemostasis. Full article
(This article belongs to the Special Issue Molecular Research on Nanotoxicology)
Show Figures

Figure 1

Figure 1
<p>Representative MNP properties as characterized using TEM and DLS. (<b>A</b>) TEM micrographs of bare and coated MNPs. (<b>B</b>) Histogram representing the distribution of individual particle sizes obtained from TEM micrographs (n = 100 particles). (<b>C</b>) The size distribution of magnetic particle clusters was determined using DLS measurements (average diameter, n = 3).</p>
Full article ">Figure 2
<p>Representative thermograms of bare and CD-modified MNPs. The mass loss of the bare MNPs was 3.8 wt.%. In contrast, the α-, β-, and γ-MNPs had total mass losses of 6.9, 7.5, and 11.2 wt.%, respectively.</p>
Full article ">Figure 3
<p>Circular dichroism spectra of protein-MNP. (<b>A</b>) Lysozyme, (<b>B</b>) HSA, and (<b>C</b>) α-lactalbumin in the presence of bare, α-, β-, and γ-MNP. All measurements are repeated three times.</p>
Full article ">Figure 4
<p>The interaction of HSA with bare and modified MNPs was investigated by quenching the intrinsic fluorescence of HSA in the presence of varied concentrations of nanoparticles. (<b>A</b>) Fluorescence emission spectra were generated by titrating the HSA solution with increasing concentrations of MNPs (0, 10, 20, 30, 40, 50, 60, and 70 μg/mL). (<b>B</b>) The plot yielded the number of binding sites (n) and the binding constant (K<sub>a</sub>), with data derived from the logarithmic relationship log [(F<sub>0</sub> − F)/(F − F<sub>s</sub>)] vs. log [S]. Here, RFU stands for relative fluorescence units, [S] represents the concentration of MNPs, F<sub>0</sub> corresponds to the relative fluorescence intensity (F) of the protein solution without MNPs, and F<sub>s</sub> indicates the relative fluorescence intensity of the protein when fully saturated with MNPs. Data represent mean ± 1 SD, n ≥ 3.</p>
Full article ">Figure 5
<p>Representative BCA assay results illustrating adsorbed protein quantities. (<b>A</b>). Adsorbed protein concentration (μg/μL). (<b>B</b>). Adsorbed protein per surface area (μg/mm<sup>2</sup>). Statistical analysis was conducted using one-way ANOVA, followed by a post hoc Tukey’s HSD test. Where * and ** indicate statistical significance of <span class="html-italic">p</span> &lt; 0.001 and <span class="html-italic">p</span> &lt; 0.05, respectively. The data are the mean ± 1 SD, n ≥ 3.</p>
Full article ">Figure 6
<p>The presence of MNPs reduces clotting time driven by plasma proteins. Clotting time in whole blood as measured by rotational thromboelastometry (ROTEM) with extrinsic activation. Data on the left side of the dotted line represents controls conducted and published by our group [<a href="#B27-ijms-25-10710" class="html-bibr">27</a>]. Results were compared across groups with repeated measures ANOVA to compare differences within biological replicates across groups, and paired t-tests were used for pairwise comparisons to the water control (* <span class="html-italic">p</span> &lt; 0.05). Comparisons not shown were not statistically significant.</p>
Full article ">Figure 7
<p>C3a ELISA results of MNP-depleted plasma from whole blood hemocompatibility experiments. Whole blood from n = 3 healthy donors was incubated with 0.18 mg/mL of each MNP formulation, and MNP-depleted plasma was assayed for C3a with a commercial ELISA. Data on the left side of the dotted line is from a separate set of experiments, previously published by our group [<a href="#B27-ijms-25-10710" class="html-bibr">27</a>], and shapes reflect biological replicates. Results were compared across groups with repeated measures ANOVA to compare differences within biological replicates across groups, and paired t-tests were used for pairwise comparisons to the water control (* <span class="html-italic">p</span> &lt; 0.05). Comparisons not shown were not statistically significant.</p>
Full article ">Figure 8
<p>CD-coated MNP exposure does not impact complete blood counts or hemolysis. Whole blood from N = 3 healthy donors was incubated with 0.18 mg/mL of each MNP formulation. Sysmex XN-550 hematology analyzer results for (<b>A</b>) white blood cells, (<b>B</b>) red blood cells (<b>C</b>) platelets, (<b>D</b>) hemoglobin, and (<b>E</b>) mean corpuscular volume of red cells are shown. Hemolysis was assayed in MNP-depleted plasma via the Harboe method (<b>F</b>). Data on the left side of the dotted line is from a separate set of experiments, previously published by our group [<a href="#B27-ijms-25-10710" class="html-bibr">27</a>], and shapes reflect biological replicates. Results were compared across groups with repeated measures ANOVA to compare differences within biological replicates across groups, and paired t-tests were used for pairwise comparisons to the water control. Comparisons not shown were not statistically significant.</p>
Full article ">Figure 9
<p>Platelet function is not impaired by CD-coated MNP exposure. Whole blood from n = 3 healthy donors was incubated with 0.18 mg/mL of each type of MNP. (<b>A</b>) Baseline platelet activation reflected by the surface expression of CD62P detected by flow cytometry. Percentage of CD62P+ platelets displayed. (<b>B</b>) Platelet degranulation in response to 10 μM of ADP, reflected by the surface expression of CD62P, detected by flow cytometry. Baseline activation was subtracted from the % of CD62P+ platelets to yield an increase in the degranulated platelets, as a measure of the platelet response. (<b>C</b>) Platelet function in coagulation reflected by the ROTEM maximum clot firmness and (<b>D</b>) clot formation time. Horizontal dashed lines indicate normal ranges as per the manufacturer’s information, and shapes reflect biological replicates. Data on the left side of the dotted line is from a separate set of experiments, previously published by our group [<a href="#B27-ijms-25-10710" class="html-bibr">27</a>]. Results were compared across groups with repeated measures ANOVA to compare differences within biological replicates across groups, and paired t-tests were used for pairwise comparisons to the water control (* <span class="html-italic">p</span> &lt; 0.05). Comparisons not shown were not statistically significant.</p>
Full article ">Scheme 1
<p>A schematic representation of the study. MNPs were functionalized with different types of CDs, including α-, β-, and γ-CD. A range of characterization techniques was employed, including TEM, DLS, TGA, and surface zeta potential measurements. Fluorescence spectroscopy and circular dichroism were utilized to study the interactions between proteins and the modified surfaces. The binding of human plasma proteins to CD-coated MNPs was evaluated, and hemocompatibility with human whole blood was assessed.</p>
Full article ">
15 pages, 2590 KiB  
Article
A Machine Learning Model for Predicting the Propagation Rate Coefficient in Free-Radical Polymerization
by Yiming Wang, Yue Fang, Haifan Zhou and Hanyu Gao
Molecules 2024, 29(19), 4694; https://doi.org/10.3390/molecules29194694 - 3 Oct 2024
Viewed by 674
Abstract
The propagation rate coefficient (kp) is one of the most crucial kinetic parameters in free-radical polymerization (FRP) as it directly governs the rate of polymerization and the resulting molecular weight distribution. The kp in FRP can typically be obtained [...] Read more.
The propagation rate coefficient (kp) is one of the most crucial kinetic parameters in free-radical polymerization (FRP) as it directly governs the rate of polymerization and the resulting molecular weight distribution. The kp in FRP can typically be obtained through experimental measurements or quantum chemical calculations, both of which can be time consuming and resource intensive. Herein, we developed a machine learning model based solely on the structural features of monomers involved in FRP, utilizing molecular embedding and a Lasso regression algorithm to predict kp more efficiently and accurately. The result shows that the model achieves a mean absolute percentage error (MAPE) of only 5.49% in the predictions for four new monomers, which indicates that the model exhibits strong generalization capabilities and provides reliable and robust predictions. In addition, this model can accurately predict the influence of the ester side chain length of (meth)acrylates on kp, aligning well with established scientific knowledge. This approach offers a straightforward and practical model for other researchers to rapidly obtain accurate kp values by employing monomer structural information. The model is sufficiently general to apply to a wide range of (meth)acrylate and butadiene FRP monomers, thereby supporting kinetic modeling of polymerization reactions. Full article
(This article belongs to the Special Issue Deep Learning in Molecular Science and Technology)
Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Results of the fitting analyses for predicting ln(<span class="html-italic">k</span><sub>p</sub>)<sup>25°C</sup> versus experimental ln(<span class="html-italic">k</span><sub>p</sub>)<sup>25°C</sup>: (<b>a</b>) multivariate linear regression model; (<b>b</b>) Lasso regression model; (<b>c</b>) ridge regression model; (<b>d</b>) Bayesian ridge regression model [<a href="#B13-molecules-29-04694" class="html-bibr">13</a>].</p>
Full article ">Figure 1 Cont.
<p>Results of the fitting analyses for predicting ln(<span class="html-italic">k</span><sub>p</sub>)<sup>25°C</sup> versus experimental ln(<span class="html-italic">k</span><sub>p</sub>)<sup>25°C</sup>: (<b>a</b>) multivariate linear regression model; (<b>b</b>) Lasso regression model; (<b>c</b>) ridge regression model; (<b>d</b>) Bayesian ridge regression model [<a href="#B13-molecules-29-04694" class="html-bibr">13</a>].</p>
Full article ">Figure 2
<p>Regression models trained by Reydt et al. (<b>a</b>) All monomers (<span class="html-italic">R</span><sup>2</sup> = 0.7221, RMSE = 1.0125); (<b>b</b>) (meth)acrylates, styrene, and acrylonitrile (<span class="html-italic">R</span><sup>2</sup> = 0.9855, RMSE = 0.2269) [<a href="#B13-molecules-29-04694" class="html-bibr">13</a>].</p>
Full article ">Figure 3
<p>APE distribution of predicted <span class="html-italic">k</span><sub>p</sub><sup>25 °C</sup> and experimental <span class="html-italic">k</span><sub>p</sub><sup>25 °C</sup> for (<b>a</b>) multivariate linear regression; (<b>b</b>) Lasso regression; (<b>c</b>) ridge regression; and (<b>d</b>) Bayesian ridge regression.</p>
Full article ">Figure 4
<p>Predictive results on the test dataset: (<b>a</b>) multivariate linear regression; (<b>b</b>) Lasso regression; (<b>c</b>) ridge regression; (<b>d</b>) Bayesian ridge regression.</p>
Full article ">Figure 4 Cont.
<p>Predictive results on the test dataset: (<b>a</b>) multivariate linear regression; (<b>b</b>) Lasso regression; (<b>c</b>) ridge regression; (<b>d</b>) Bayesian ridge regression.</p>
Full article ">Figure 5
<p>Fitting analyses on the training set for (<b>a</b>) <span class="html-italic">E</span><sub>A</sub>; (<b>b</b>) ln(<span class="html-italic">A</span>).</p>
Full article ">Figure 6
<p>APE of predicted and experimental values on the test dataset for (<b>a</b>) <span class="html-italic">E</span><sub>A</sub>; (<b>b</b>) <span class="html-italic">A</span>.</p>
Full article ">
16 pages, 4993 KiB  
Article
A Numerical Framework of Simulating Flow-Induced Deformation during Liquid Composite Moulding
by Hatim Alotaibi, Constantinos Soutis, Dianyun Zhang and Masoud Jabbari
J. Compos. Sci. 2024, 8(10), 401; https://doi.org/10.3390/jcs8100401 - 3 Oct 2024
Viewed by 530
Abstract
Fibre deformation (or shearing of yarns) can develop during the liquid moulding of composites due to injection pressures or polymerisation (cross-linking) reactions (e.g., chemical shrinkage). On that premise, this may also induce potential residual stress–strain, warpage, and design defects in the composite part. [...] Read more.
Fibre deformation (or shearing of yarns) can develop during the liquid moulding of composites due to injection pressures or polymerisation (cross-linking) reactions (e.g., chemical shrinkage). On that premise, this may also induce potential residual stress–strain, warpage, and design defects in the composite part. In this paper, a developed numerical framework is customised to analyse deformations and the residual stress–strain of fibre (at a micro-scale) and yarns (at a meso-scale) during a liquid composite moulding (LCM) process cycle (fill and cure stages). This is achieved by linking flow simulations (coupled filling–curing simulation) to a transient structural model using ANSYS software. This work develops advanced User-Defined Functions (UDFs) and User-Defined Scalers (UDSs) to enhance the commercial CFD code with extra models for chemorheology, cure kinetics, heat generation, and permeability. Such models will be hooked within the conservation equations in the thermo-chemo-flow model and hence reflected by the structural model. In doing so, the knowledge of permeability, polymerisation, rheology, and mechanical response can be digitally obtained for more coherent and optimised manufacturing processes of advanced composites. Full article
Show Figures

Figure 1

Figure 1
<p>The framework of numerical one-way fluid–structure interaction (FSI)—linking CFD to FEA.</p>
Full article ">Figure 2
<p>Description for in-plane deformation of yarns subjected to injection pressures or resin curing.</p>
Full article ">Figure 3
<p>Resin cure profiles at different cure temperatures: 45 °C, 60 °C, and 75 °C.</p>
Full article ">Figure 4
<p>An example of flow–structural linkage simulation at an injection pressure of <math display="inline"><semantics> <mrow> <mn>50</mn> <mspace width="3.33333pt"/> <mi>kPa</mi> </mrow> </semantics></math> to demonstrate resin impregnation, yarn residual stress and strain fields (von-Mises), and yarn total deformation, within a representative unit cell (RUC) of a plain weave.</p>
Full article ">Figure 5
<p>An example of cure-structural linkage simulation at an injection pressure of <math display="inline"><semantics> <mrow> <mn>50</mn> <mspace width="3.33333pt"/> <mi>kPa</mi> </mrow> </semantics></math> and a cure temperature of 60 °C to demonstrate resin cure, yarn residual stress and strain fields (von-Mises), and yarn total deformation, within a representative unit cell (RUC) of a plain weave.</p>
Full article ">
Back to TopTop