The concentration and detection of molecular biomarkers remain as a challenge to develop point-of... more The concentration and detection of molecular biomarkers remain as a challenge to develop point-of-care diagnostic devices. An electric field induced concentration has been studied for such purposes but with limited success due to limited efficacy. This paper presents a computational study for investigating the molecular concentration and retention efficacy of single nanowire (SNW) and dendritic nanotip (DNT) sensors. Our computational results indicate that compared to a DNT, the SNW sensor produces higher dielectrophoretic (DEP) forces in the vicinity of the terminal end of the tip. Furthermore, the magnitude of the DEP force increases exponentially as the diameter of the SNW is decreased, resulting in a further improved retention efficacy of NPs. However, the SNW sensor's concentration efficacy was not much improved for NPs smaller than 10 nm diameter when the nanowire diameter was reduced from 500 to 50 nm. Compared to the SNW, the DNT sensor showed improved concentration efficacy due to multiple points of electric field concentrations, which retard the exponential decay of the DEP force resulting in a greater widespread region where the DEP force dominates the Brownian motion forces. When oligonucleotides are used as a target particle, the DEP force can be used to elongate oligonucleotides to further enhance the concentration and retention efficacy.
The ion flow in nanochannels is investigated by using nanochannels in an open configuration that ... more The ion flow in nanochannels is investigated by using nanochannels in an open configuration that allows the direct observation of fluid diffusion through an optical microscope. An “open nanochannel” is a channel with the top open to air such that fluidics can be introduced from both the entrance and the top of the channels. The experimental results showed that the diffusion length of the potassium chloride and phosphate buffer decreased with their concentration. The observed behaviors were analyzed by the contact angle variation due to the electrowetting phenomena involving the interaction between electrical double layer and counter-ions in the solution.
Integrating materials and manufacturing innovation, Jul 29, 2021
Design of additively manufactured metallic parts requires computational models that can predict t... more Design of additively manufactured metallic parts requires computational models that can predict the mechanical response of parts considering the microstructural, manufacturing, and operating conditions. This article documents our response to Air Force Research Laboratory (AFRL) Additive Manufacturing Modeling Challenge 3, which asks the participants to predict the mechanical response of tensile coupons of IN625 as function of microstructure and manufacturing conditions. A representative volume element (RVE) approach was coupled with a crystal plasticity material model solved within the Fast Fourier Transformation (FFT) framework for mechanics to address the challenge. During the competition, material model calibration proved to be a challenge, prompting the introduction in this manuscript of an advanced material model identification method using proper generalized decomposition (PGD). Finally, a mechanistic reduced order method called Self-consistent Clustering Analysis (SCA) is shown as a possible alternative to the FFT method for solving these problems. Apart from presenting the response analysis, some physical interpretation and assumptions associated with the modeling are discussed.
Integrating materials and manufacturing innovation, May 11, 2021
Challenge 4 of the Air Force Research Laboratory additive manufacturing modeling challenge series... more Challenge 4 of the Air Force Research Laboratory additive manufacturing modeling challenge series asks the participants to predict the grain-average elastic strain tensors of a few specific challenge grains during tensile loading, based on experimental data and extensive characterization of an IN625 test specimen. In this article, we present our strategy and computational methods for tackling this problem. During the competition stage, a characterized microstructural image from the experiment was directly used to predict the mechanical responses of certain challenge grains with a genetic algorithm-based material model identification method. Later, in the post-competition stage, a proper generalized decomposition (PGD)-based reduced order method is introduced for improved material model calibration. This data-driven reduced order method is efficient and can be used to identify complex material model parameters in the broad field of mechanics and materials science. The results in terms of absolute error have been reported for the original prediction and re-calibrated material model. The predictions show that the overall method is capable of handling large-scale computational problems for local response identification. The re-calibrated results and speed-up show promise for using PGD for material model calibration.
The concentration and detection of molecular biomarkers remain as a challenge to develop point-of... more The concentration and detection of molecular biomarkers remain as a challenge to develop point-of-care diagnostic devices. An electric field induced concentration has been studied for such purposes but with limited success due to limited efficacy. This paper presents a computational study for investigating the molecular concentration and retention efficacy of single nanowire (SNW) and dendritic nanotip (DNT) sensors. Our computational results indicate that compared to a DNT, the SNW sensor produces higher dielectrophoretic (DEP) forces in the vicinity of the terminal end of the tip. Furthermore, the magnitude of the DEP force increases exponentially as the diameter of the SNW is decreased, resulting in a further improved retention efficacy of NPs. However, the SNW sensor's concentration efficacy was not much improved for NPs smaller than 10 nm diameter when the nanowire diameter was reduced from 500 to 50 nm. Compared to the SNW, the DNT sensor showed improved concentration efficacy due to multiple points of electric field concentrations, which retard the exponential decay of the DEP force resulting in a greater widespread region where the DEP force dominates the Brownian motion forces. When oligonucleotides are used as a target particle, the DEP force can be used to elongate oligonucleotides to further enhance the concentration and retention efficacy.
The ion flow in nanochannels is investigated by using nanochannels in an open configuration that ... more The ion flow in nanochannels is investigated by using nanochannels in an open configuration that allows the direct observation of fluid diffusion through an optical microscope. An “open nanochannel” is a channel with the top open to air such that fluidics can be introduced from both the entrance and the top of the channels. The experimental results showed that the diffusion length of the potassium chloride and phosphate buffer decreased with their concentration. The observed behaviors were analyzed by the contact angle variation due to the electrowetting phenomena involving the interaction between electrical double layer and counter-ions in the solution.
Integrating materials and manufacturing innovation, Jul 29, 2021
Design of additively manufactured metallic parts requires computational models that can predict t... more Design of additively manufactured metallic parts requires computational models that can predict the mechanical response of parts considering the microstructural, manufacturing, and operating conditions. This article documents our response to Air Force Research Laboratory (AFRL) Additive Manufacturing Modeling Challenge 3, which asks the participants to predict the mechanical response of tensile coupons of IN625 as function of microstructure and manufacturing conditions. A representative volume element (RVE) approach was coupled with a crystal plasticity material model solved within the Fast Fourier Transformation (FFT) framework for mechanics to address the challenge. During the competition, material model calibration proved to be a challenge, prompting the introduction in this manuscript of an advanced material model identification method using proper generalized decomposition (PGD). Finally, a mechanistic reduced order method called Self-consistent Clustering Analysis (SCA) is shown as a possible alternative to the FFT method for solving these problems. Apart from presenting the response analysis, some physical interpretation and assumptions associated with the modeling are discussed.
Integrating materials and manufacturing innovation, May 11, 2021
Challenge 4 of the Air Force Research Laboratory additive manufacturing modeling challenge series... more Challenge 4 of the Air Force Research Laboratory additive manufacturing modeling challenge series asks the participants to predict the grain-average elastic strain tensors of a few specific challenge grains during tensile loading, based on experimental data and extensive characterization of an IN625 test specimen. In this article, we present our strategy and computational methods for tackling this problem. During the competition stage, a characterized microstructural image from the experiment was directly used to predict the mechanical responses of certain challenge grains with a genetic algorithm-based material model identification method. Later, in the post-competition stage, a proper generalized decomposition (PGD)-based reduced order method is introduced for improved material model calibration. This data-driven reduced order method is efficient and can be used to identify complex material model parameters in the broad field of mechanics and materials science. The results in terms of absolute error have been reported for the original prediction and re-calibrated material model. The predictions show that the overall method is capable of handling large-scale computational problems for local response identification. The re-calibrated results and speed-up show promise for using PGD for material model calibration.
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Papers by Wing Kam Liu