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Modeling the effects of salt concentration on aqueous and organic electrolytes

npj Computational Materials 2023年第1期9卷 546-552页

作者： Stephanie C.C.van der Lubbe Pieremanuele Canepa Department of Materials Science and Engineering National University of Singapore9 Engineering Drive 1117575 SingaporeSingapore Department of Chemical and Biomolecular Engineering National University of Singapore4 Engineering Drive 4117585 SingaporeSingapore Department of Electrical and Computer Engineering and Texas Center for Superconductivity University of HoustonHoustonTX 77204USA

Understanding the thermodynamic properties of electrolyte solutions is of vital importance for a myriad of physiological and technological *** mean activity coefficientγ±is associated with the deviation of an electrolyte solution from its ideal behavior and may be obtained by combining the Debye-Hückel(DH)and Born(B)***,the DH and B equations depend on the concentration and temperature-dependent static permittivity of the solutionεr(c,T)and the size of the solvated ions ri,whose experimental data is often not ***,we use a combination of molecular dynamics and density functional theory to predictεr(c,T)and ri,which enables us to apply the DH and B equations to any technologically relevant aqueous and nonaqueous electrolyte at any concentration and temperature of interest.

关键词： equations electrolyte apply

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Introducing Multidisciplinary engineering Technology and Programing for High School Students Through Summer Program 54

Introducing Multidisciplinary Engineering Technology and Pro...

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54th IEEE Frontiers in Education Conference, FIE 2024

作者： Wang, Sujing Xu, Qiang Andrei, Stefan Lamar University Department of Computer Science BeaumontTX United States Lamar University Dan F. Smith Department of Chemical and Biomolecular Engineering BeaumontTX United States Oregon Institute of Technology Computer Systems Engineering Technology Department Klamath FallsOR United States

ISBN: (纸本)9798350351507

This innovative practice full paper introduces the design, organization, and evaluation of two one-week summer camps to introduce multi-disciplinary engineering technology and programming to high school students. This program is designed to increase students' interests and knowledge of multi-disciplinary engineering and computing, foster collaboration, and build community. By engaging in our program, students not only think critically and enhance their problem-solving skills but also develop foundational proficiency in engineering and computational thinking. This crucial skill set is recognized as essential in the 21st century, opening doors to enriching engineering capabilities and creating diverse opportunities for pursuing careers in multiple engineering fields. This paper introduces the organizational structure, coordination strategies, core curriculum instructions, and the program assessment result which demonstrates a substantial improvement in knowledge and increased interest in computing and engineering across all participant cohorts. The primary objective of this paper is to furnish a detailed guide, equipping institutions with the essential information required to successfully host similar summer programs. Furthermore, it aims to catalyze endeavors geared towards enhancing young students' participation in the fields of computing and engineering. © 2024 IEEE.

关键词： MATLAB

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Advances of photothermal chemistry in photocatalysis,thermocatalysis,and synergetic photothermocatalysis for solar-to-fuel generation

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Nano Research 2022年第12期15卷 9985-10005页

作者： Minmin Gao Tianxi Zhang Ghim Wei Ho Department of Electrical and Computer Engineering National University of Singapore4 Engineering Drive 3Singapore117583Singapore Department of Chemical and Biomolecular Engineering National University of Singapore4 Engineering Drive 4Singapore117585Singapore Institute of Materials Research and Engineering Agency for ScienceTechnology and Research(A*STAR)3 Research LinkSingapore117602Singapore

The urgency of reducing pollutants and greenhouse gas emissions while maintaining fuel supply for the development of society remains one of the greatest *** energy,a clean and sustainable energy resource,can be converted into fuels through solar-driven catalysis,and this provides an attractive solution for future energy *** current development of photothermal catalysis(PTC)based on the integration of solar thermal and photochemical contributions is becoming increasingly popular for full spectrum *** combination of the thermochemical and photochemical processes synergistically drives the catalytic reactions efficiently under relatively mild *** this review,the mechanisms of PTC are classified based on driving forces and the benefits of photothermal effects in different PTC reactions are ***,the techniques for differentiating and quantifying the various effects of PTC,including experimental designs,thermometry characterization techniques,and computational studies,are ***,the major determinant properties and architectural designs for efficient photothermal catalysts are ***,applications for fuel generation through water splitting and carbon dioxide reduction are ***,the current challenges and future directions of PTC are *** article aims to provide a comprehensive review of the current advances in PTC along with a guide for understanding the mechanisms and rational material designs to pursue solar fuel that would diversify and increase the sustainability of our energy supply.

关键词： photothermal chemistry photocatalysis thermocatalysis water splitting CO_(2)conversion solar fuel

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Metal carbonyl loaded nanoparticles for breast cancer biomarker detection 21

Metal carbonyl loaded nanoparticles for breast cancer biomar...

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Nanoscale Imaging, Sensing, and Actuation for Biomedical Applications XXI 2024

作者： Richard, Craig Hsieh, Pei-Hsuan Confer, Matthew Bhargava, Rohit Department of Bioengineering University of Illinois at Urbana-Champaign United States Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign United States Departments of Electrical and Computer Engineering Mechanical Science and Engineering Chemical and Biomolecular Engineering Chemistry United States Cancer Center at Illinois University of Illinois at UrbanaChampaign Urbana United States

ISBN: (纸本)9781510669758

The potential advantages of infrared spectroscopic imaging for examination of cells and tissue biopsies for cancer histopathology is high, however there are fundamental limitations of FTIR that must first be addressed. One limitation is that Infrared spectroscopy lacks the sensitivity to detect biomarkers of disease directly. With cancer being one of the leading causes of death and its increasing prevalence, it is important to develop tools that can rapidly screen and enable rapid and simultaneous fingerprinting of biologic content and metabolic state. To address these limitations, probed Infrared spectroscopy is being explored. We report the successful synthesis, characterization, and application of metal carbonyl loaded nanoparticle probes for the detection of breast cancer biomarkers. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Biomarkers

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Uncertainty Analysis in Perturbation Monte Carlo Simulations of Radiative Transport 15

Uncertainty Analysis in Perturbation Monte Carlo Simulations...

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Optical Tomography and Spectroscopy of Tissue XV 2023

作者： Parsanasab, Mahsa Hayakawa, Carole Spanier, Jerome Shen, Yanning Venugopalan, Vasan Department of Chemical and Biomolecular Engineering University of California Irvine United States Beckman Laser Institute and Medical Clinic University of California Irvine United States Department of Electrical Engineering and Computer Science University of California Irvine United States

ISBN: (纸本)9781510658578

In this paper, the accuracy of perturbation Monte Carlo(pMC) estimates as a function of scattering perturbation size for spatially-resolved diffuse reflectance over a broad range of optical properties is analyzed. We also propose a methodology to predict the variation of pMC variance with perturbation size based on the data from the reference simulation alone. The results show better pMC performance when based on results from reference Monte Carlo simulations that utilize a Russian Roulette as a variance reduction method. Specifically, we demonstrate that for a proximal detector we can estimate the pMC relative error within 5% of the true value for scattering perturbations in the range of [-15%, +20%]. For a distal, our method provides relative error estimates within 20% for scattering perturbations in the range of [-8%, +15%]. Moreover, improved performance is observed in case of both proximal and distal detectors when reference simulations performed at lower (μμas′) values. This methodology is useful for the optimal design of pMC analysis of multi-spectral data sets. © 2023 SPIE.

关键词： Inverse problems

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Image-based Cutting Tool Health Diagnosis in Multi-machine Settings 6

Image-based Cutting Tool Health Diagnosis in Multi-machine S...

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6th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Mishra, Debasish Pattipati, Krishna R. Bollas, George M. Data Science and Information Systems Krea University 5655 Central Expressway Sri City517646 India Electrical and Computer Engineering University of Connecticut 371 Fairfield Way Storrs06269 United States Chemical and Biomolecular Engineering University of Connecticut 159 Discovery Drive Storrs06269 United States

Manufacturers are increasingly focused on evaluating the condition of cutting tools during machining processes to ensure high-quality production. This study introduces a method for accurately and cost-effectively diagnosing the health of cutting tools. The method employs image similarity indices-such as Bhattacharyya distance, Fourier transform, and Cosine similarity-to compute indicators of tool health from wavelet coefficient maps of cutting signals. These indicators are then used in an unsupervised machine learning (ML) algorithm, agglomerative clustering, to categorize the tool health state. To validate the method, run-to-failure tests of the tool were conducted on two different machines with varying specifications and machining parameters. The results demonstrate a strong association between the computed indicators and the tool health, regardless of the differences between the two machines and their cutting settings. This is due to the reliance of the method on the physics of machining processes to derive the health indicators. The ML model effectively categorizes the progressive health of cutting tools and provides easily interpretable results on the health of the tool due to the explainability of the indicators. © 2025 The Authors. Published by Elsevier B.V.

关键词： Clustering Machine Learning Precision machining Similarity Tool condition monitoring Unsupervised

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PESI: Paratope-Epitope Set Interaction for SARS-CoV-2 Neutralization Prediction

PESI: Paratope-Epitope Set Interaction for SARS-CoV-2 Neutra...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Wan, Zhang Lin, Zhuoyi Rashid, Shamima Ng, Shaun Yue-Hao Yin, Rui Senthilnath, J. Kwoh, Chee-Keong Nanyang Technological University School of Computer Science and Engineering Singapore Institute for Infocomm Research Agency for Science Singapore Nanyang Technological University School of Chemical and Biomolecular Engineering Singapore University of Florida Department of Health Outcome and Biomedical Informatics GainesvilleFL United States

ISBN: (纸本)9798350337488

Prediction of neutralization antibodies is important for the development of effective vaccines and antibody-based therapeutics. Traditional methods rely on features based on first principles derived from the binding interface. However, they are burdened by arduous data preprocessing from a limited quantity of protein structures. In comparison, deep learning allows automatic substructure characterization and representation without hand-crafted feature engineering. In particular, large language models (LLMs) based method predicts neutralization using Fv sequences of antibody and antigen. Despite LLM's success, incorporating full-length Fv sequences suffers from: 1) inaccurate sequence-level labels in existing datasets, 2) inefficient modeling due to noisy non-contributing motifs, and 3) ignorance of non-bonded interactions that play a key role in facilitating epitope-paratope pairing. In this paper, we propose a novel approach that incorporates only the paratope and epitope for antibody-antigen neutralization prediction while adopting a novel set modeling that regards the paratope and epitope as bags of residues. Specifically, we hand-crafted a dataset containing neutralizing paratope-epitope pairs where epitopes are potentially generalizable to future unseen variants of SARS-CoV-2. Training on such a dataset enables deep learning models to predict neutralizing antibodies for prospective mutated variants of SARS-CoV-2, meanwhile addressing the problem of inaccurate sequence-level labels. A higher modeling efficiency is also achieved by disregarding non-contributing motifs. Furthermore, we also propose paratope-epitope set interaction (PESI), a set modeling model inspired by first principles that learns intra-inter non-covalent interactions through a global attention mechanism. To validate PESI, we perform a 10-fold cross-validation on our dataset. Experimental results show that PESI achieves a more balanced overall performance and a significant improvement on MCC as c

关键词： deep learning paratope-epitope interaction SARS-CoV-2 set modeling Transformer

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Metal Enhanced Fluorescence Microarray Biochip using Glancing Angle Deposited Ag nanorods 13

Metal Enhanced Fluorescence Microarray Biochip using Glancin...

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13th International Conference on Information and Communication Technology Convergence, ICTC 2022

作者： Badshah, Mohsin Ali Lee, Seong-Min Jin, Chengjun Byeon, Seongyoung Hussain, Tasadduq Ali, Muzahir Kim, Seok-Min University of California Dept. of Chemical and Biomolecular Engineering Irvine United States Chung-Ang University Dept. Computer Science and Engineering Seoul Korea Republic of

ISBN: (数字)9781665499392

ISBN: (纸本)9781665499392

Glancing angle deposition (GLAD) is a physical vapor deposition process in which the substrate is placed to have a large incidence angle (>75°, angle between incoming flux and substrate normal). The GLAD process can be considered a promising method to fabricate metallic nanostructure on a large area at a low cost. GLAD Ag nanorods were fabricated and their fluorescence enhancement performances were examined. A practical antibody chip was realized using a 500 nm thick vertical Ag nanorods substrate. To overcome the limitations of GLAD Ag nanorod MEF substrate, Ag nanorods on micro post array substrate were also investigated. © 2022 IEEE.

关键词： Biochips

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Evolution-guided Bayesian optimization for constrained multi-objective optimization in self-driving labs

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npj Computational Materials 2024年第1期10卷 2171-2181页

作者： Andre K.Y.Low Flore Mekki-Berrada Abhishek Gupta Aleksandr Ostudin Jiaxun Xie Eleonore Vissol-Gaudin Yee-Fun Lim Qianxiao Li Yew Soon Ong Saif A.Khan Kedar Hippalgaonkar School of Materials Science and Engineering Nanyang Technological UniversitySingapore 639798Singapore Institute of Materials Research and Engineering(IMRE) Agency for ScienceTechnology and Research(A*STAR)2 Fusionopolis WayInnovis#08-03Singapore 138634Republic of Singapore Department of Chemical and Biomolecular Engineering National University of SingaporeSingapore 117585Singapore School of Mechanical Sciences Indian Institute of Technology GoaGoa 403401India Institute of Sustainability for Chemicals Energy and Environment(ISCE2)Agency for ScienceTechnology and Research(A*STAR)1 Pesek RoadSingapore 627833Republic of Singapore Department of Mathematics National University of SingaporeSingapore 119077Singapore Institute for Functional Intelligent Materials National University of SingaporeSingapore 117544Singapore School of Computer Science and Engineering Nanyang Technological UniversitySingapore 639798Singapore

The development of automated high-throughput experimental platforms has enabled fast sampling of high-dimensional decision *** reach target properties efficiently,these platforms are increasingly paired with intelligent experimental ***,current optimizers show limitations in maintaining sufficient exploration/exploitation balance for problems dealing with multiple conflicting objectives and complex ***,we devise an Evolution-Guided Bayesian Optimization(EGBO)algorithm that integrates selection pressure in parallel with a q-Noisy Expected Hypervolume Improvement(qNEHVI)optimizer;this not only solves for the Pareto Front(PF)efficiently but also achieves better coverage of the PF while limiting sampling in the infeasible space.

关键词： optimization driving constrained

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Single-shot quantitative phase gradient microscopy using physics-based untrained neural network phase retrieval 11

Single-shot quantitative phase gradient microscopy using phy...

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Quantitative Phase Imaging XI 2025

作者： Hur, Sun Woong Sengupta, Sourya Kwon, Minsung Manoharaan, Revathi Anastasio, Mark Bhargava, Rohit Department of Bioengineering University of Illinois Urbana-ChampaignIL United States Department of Electrical and Computer Engineering University of Illinois Urbana-ChampaignIL United States Beckman Institute for Advanced Science and Technology University of Illinois Urbana-ChampaignIL United States Department of Computer Science Carle-Illinois College of Medicine University of Illinois Urbana-ChampaignIL United States Department of Chemical and Biomolecular Engineering Mechanical Science and Engineering and Chemistry Cancer Center at Illinois University of Illinois Urbana-ChampaignIL United States CZ Biohub Chicago LLC IL United States

ISBN: (纸本)9781510684065

Single-shot quantitative phase gradient microscopy (ss-QPGM) enables quantitative phase imaging (QPI) based on differential interference contrast (DIC) microscopy. In ss-QPGM, the analyzer is replaced by a quarter-wave plate and a polarization camera, allowing for phase gradient extraction from a single intensity measurement. The quantitative phase image is reconstructed via numerical integration, but direct integration along a single axis introduces artifacts and phase errors due to missing frequency response along the axis of orthogonal to the shear axis and noise-induced streak lines. In this work, we propose a phase retrieval method for ss-QPGM using an untrained neural network (UNN), which integrates physical priors without requiring paired ground truth and input datasets, overcoming the limitations of traditional regularization methods such as Tikhonov regularization and the alternating direction method of multipliers (ADMM) with total variation (TV) regularization. We demonstrate that the UNN approach not only offers superior phase reconstruction but also alleviates artifacts induced by missing frequency response along the axis and noise-induced streak lines, as shown by phase images of a calibrated target and biological samples. © 2025 SPIE.

关键词： Image reconstruction

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