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A simple agglutination system for rapid antigen detection from large sample volumes with enhanced sensitivity

Analytica Chimica Acta 2023年第1期1277卷 341674页

作者： Wang, Qin Panpradist, Nuttada Kotnik, Jack Henry Willson, Richard C. Kourentzi, Katerina Chau, Zoe L. Liu, Joanne K. Lutz, Barry R. Lai, James J. Department of Bioengineering University of Washington SeattleWA98195-5061 United States Department of Chemical and Biomolecular Engineering University of Houston HoustonTX77204 United States Department of Materials Science and Engineering National Taiwan University of Science and Technology Taipei106335 Taiwan

Lateral flow assays (LFAs) provide a simple and quick option for diagnosis and are widely adopted for point-of-care or at-home tests. However, their sensitivity is often limited. Most LFAs only allow 50 μL samples while various sample types such as saliva could be collected in much larger volumes. Adapting LFAs to accommodate larger sample volumes can improve assay sensitivity by increasing the number of target analytes available for detection. Here, a simple agglutination system comprising biotinylated antibody (Ab) and streptavidin (SA) is presented. The Ab and SA agglutinate into large aggregates due to multiple biotins per Ab and multiple biotin binding sites per SA. Dynamic light scattering (DLS) measurements showed that the agglutinated aggregate could reach a diameter of over 0.5 μm and over 1.5 μm using poly-SA. Through both experiments and Monte Carlo modeling, we found that high valency and equivalent concentrations of the two aggregating components were critical for successful agglutination. The simple agglutination system enables antigen capture from large sample volumes with biotinylated Ab and a swift transition into aggregates that can be collected via filtration. Combining the agglutination system with conventional immunoassays, an agglutination assay is proposed that enables antigen detection from large sample volumes using an in-house 3D-printed device. As a proof-of-concept, we developed an agglutination assay targeting SARS-CoV-2 nucleocapsid antigen for COVID-19 diagnosis from saliva. The assay showed a 10-fold sensitivity enhancement when increasing sample volume from 50 μL to 2 mL, with a final limit of detection (LoD) of 10 pg mL−1 (∼250 fM). The assay was further validated in negative saliva spiked with gamma-irradiated SARS-CoV-2 and showed an LoD of 250 genome copies per μL. The proposed agglutination assay can be easily developed from existing LFAs to facilitate the processing of large sample volumes for improved sensitivity. © 2023

关键词： COVID-19

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Symbolic regression-based hybrid models for a manufacturing process

Symbolic regression-based hybrid models for a manufacturing ...

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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Seulki Han Debasish Mishra Krishna R. Pattipati George M. Bollas Department of Chemical & Biomolecular Engineering University of Connecticut Storrs CT USA Data Science & Information Systems Krea University Sri City Andhra Pradesh India Department of Electrical & Computer Engineering University of Connecticut Storrs CT USA

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

Hybrid models are increasingly employed to simulate intricate physical processes by combining domain expertise embedded in physics-based models with process measurements-based data. The combination of domain knowledge and measurement data results in hybrid models that lead to high-accuracy decisions, extrapolation capabilities, and compliance with basic physical laws. Surrogate modeling, one aspect of hybrid modeling, involves a simplified model to capture the physics in a process, emphasizing an adaptable mathematical expression. This study applies hybrid modeling to address the intricate tool wear process in precision machining by developing a recursive model using symbolic regression.

关键词： Adaptation models Manufacturing processes Machining Predictive models Feature extraction Mathematical models Data models Time measurement Information technology Physics

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Cigarette butt filter as membrane material with tannic acid and FeCl₃ additives for improve antifouling properties

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Case Studies in chemical and Environmental engineering 2025年 11卷

作者： Luthfiana, Anisa Mulyati, Sri Arahman, Nasrul Bilad, Muhammad Roil Aulia, Muhammad Prayogie Doctoral Program School of Engineering Science Universitas Syiah Kuala Banda Aceh Indonesia Department of Chemical Engineering Universitas Syiah Kuala Banda Aceh Indonesia Department of Chemical and Process Engineering Faculty of Integrated Technologies Universiti Brunei Darussalam Bandar Seri Begawan Brunei Darussalam Department of Chemical Science and Engineering Kobe University Kobe Japan

Membrane fouling remains a critical challenge in wastewater treatment, particularly in applications involving oil-water emulsions. This study addresses this issue by fabricating antifouling membranes from repurposed cigarette filter waste, modified with tannic acid and FeCl₃ (ferric chloride) coatings. These modifications enhance membrane hydrophilicity, antifouling properties, flux recovery, and selectivity through an environmentally friendly approach. The membranes were prepared using the non-solvent induced phase separation (NIPS) method and subsequently coated through vacuum filtration. Key performance metrics included pure water flux, oil emulsion selectivity, and antifouling properties. The antifouling mechanism was attributed to the hydrophilic and protective layers formed by the tannic acid and FeCl₃ modifications, which reduced fouling and improved flux recovery. Characterization revealed that the tannic acid and FeCl₃ modifications created a hydrophilic layer with uniform pore distribution, leading to an oil rejection rate of up to 97 % and an increased flux recovery ratio of 85 %, compared to 65 % in unmodified membranes. The results highlight the potential of waste-derived membranes as a sustainable alternative for industrial wastewater treatment, aligning with the principles of circular economy and green chemistry. Future work should explore long-term stability, surface charge effects, and optimization of additive concentrations to enhance performance and antifouling efficiency further. © 2025 The Authors

关键词： Antifouling membrane Cigarette filter waste FeCl3 Oil-water emulsion tannic acid

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An economic and resource-based tool to evaluate carbon mineralization processes 15

An economic and resource-based tool to evaluate carbon miner...

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15th Greenhouse Gas Control Technologies Conference, GHGT 2021

作者： Woodall, Caleb M. Wilcox, Jennifer Department of Chemical Engineering Worcester Polytechnic Institute WorcesterMA01609 United States Department of Chemical and Biomolecular Engineering School of Engineering and Applied Science Kleinman Center for Energy Policy University of Pennsylvania PhiladelphiaPA United States

Carbon mineralization research, development, and commercial deployment has made many advancements through the last two decades. Still, economic limitations exist to fund further research and deployment. Now with peak interest in carbon capture and removal technologies from companies and government agencies, there exists a need to easily and consistently compare potential mineralization processes. A techno-economic analysis tool has been developed that is applicable to a wide variety of mineralization processes, providing a common methodology to compare potential projects. The tool's primary input is the desired CO2 capture per year, among other key parameters of alkaline feedstock and target product. A project's overall economics are calculated in terms of net cost per tonne CO2 captured, by balancing capital expense, operating expense, and income generated from products and tax incentives. Two example scenarios are given to demonstrate the capabilities of the tool. Future developments will be necessary to improve the tool's robustness and accuracy. © 2021 15th Greenhouse Gas Control Technologies Conference 2021, GHGT 2021. All Rights Reserved.

关键词： Carbon dioxide

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AutoTemplate: enhancing chemical reaction datasets for machine learning applications in organic chemistry

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Journal of Cheminformatics 2024年第1期16卷 74页

作者： Chen, Lung-Yi Li, Yi-Pei Department of Chemical Engineering National Taiwan University Taiwan International Graduate Program on Sustainable Chemical Science and Technology (TIGP-SCST)

Abstract: This paper presents AutoTemplate, an innovative data preprocessing protocol, addressing the crucial need for high-quality chemical reaction datasets in the realm of machine learning applications in organic chemistry. Recent advances in artificial intelligence have expanded the application of machine learning in chemistry, particularly in yield prediction, retrosynthesis, and reaction condition prediction. However, the effectiveness of these models hinges on the integrity of chemical reaction datasets, which are often plagued by inconsistencies like missing reactants, incorrect atom mappings, and outright erroneous reactions. AutoTemplate introduces a two-stage approach to refine these datasets. The first stage involves extracting meaningful reaction transformation rules and formulating generic reaction templates using a simplified SMARTS representation. This simplification broadens the applicability of templates across various chemical reactions. The second stage is template-guided reaction curation, where these templates are systematically applied to validate and correct the reaction data. This process effectively amends missing reactant information, rectifies atom-mapping errors, and eliminates incorrect data entries. A standout feature of AutoTemplate is its capability to concurrently identify and correct false chemical reactions. It operates on the premise that most reactions in datasets are accurate, using these as templates to guide the correction of flawed entries. The protocol demonstrates its efficacy across a range of chemical reactions, significantly enhancing dataset quality. This advancement provides a more robust foundation for developing reliable machine learning models in chemistry, thereby improving the accuracy of forward and retrosynthetic predictions. AutoTemplate marks a significant progression in the preprocessing of chemical reaction datasets, bridging a vital gap and facilitating more precise and efficient machine learning applicati

关键词： Atom-to-atom mapping Data preprocessing Reaction data curation Reaction template

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Multi-Objective Bayesian Optimisation Over Sparse Subspaces for Model Predictive Control of Wind Farms

SSRN

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SSRN 2024年

作者： Hoang, Kiet Tuan Boersma, Sjoerd Mesbah, Ali Imsland, Lars Struen Department of Engineering Cybernetics Norwegian University of Science and Technology Trondheim7034 Norway Biometris Group Wageningen University & Research Wageningen6708 Netherlands Department of Chemical and Biomolecular Engineering University of California BerkeleyCA94720 United States

As model mismatch is inevitable, fine-tuning advanced control strategies like model predictive control (MPC) using closed-loop performance data is critical for increased closed-loop control of wind farms (WFs). However, challenges such as conflicting WF control objectives, limited closed-loop data due to expensive experiments, and the high-dimensional design spaces of these MPC formulations make tuning non-trivial. Inspired by the notion of performance-oriented learning, we propose a multi-objective (MO) Bayesian optimisation (BO) framework over sparse subspaces to address these challenges systematically for increased closed-loop MPC performance. To show the efficacy of the BO approach, a simulation case study with a 3x3 WF is investigated where the control objective is to provide secondary frequency regulation while minimising dynamic loading for an MPC with 28 design parameters to auto-tune. Simulation results show that the proposed framework achieves a good balance between the two conflicting WF control objectives, where dynamic loading is reduced by 51.59% compared to a nominal MPC whose performance is not tuned using closed-loop data while still achieving similar tracking performance. The proposed method is general and can be applied regardless of a closed-loop control goal, WF specifications (complexity, topology, location, size), or controller formulation for multi-objective constrained control of WFs. © 2024, The Authors. All rights reserved.

关键词： Model predictive control

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Exploring Material Design Space with a Deep-Learning Guided Genetic Algorithm 28

Exploring Material Design Space with a Deep-Learning Guided ...

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28th International Conference on DNA Computing and Molecular programming, DNA 2022

作者： Chen, Kuan-Lin Schulman, Rebecca Department of Chemical and Biomolecular Engineering Johns Hopkins University BaltimoreMD United States Department of Computer Science Johns Hopkins University BaltimoreMD United States Department of Chemistry Johns Hopkins University BaltimoreMD United States

ISBN: (纸本)9783959772532

Designing complex, dynamic yet multi-functional materials and devices is challenging because the design spaces for these materials have numerous interdependent and often conflicting constraints. Taking inspiration from advances in artificial intelligence and their applications in material discovery, we propose a computational method for designing metamorphic DNA-co-polymerized hydrogel structures. The method consists of a coarse-grained simulation and a deep learning-guided optimization system for exploring the immense design space of these structures. Here, we develop a simple numeric simulation of DNA-co-polymerized hydrogel shape change and seek to find designs for structured hydrogels that can fold into the shapes of different Arabic numerals in different actuation states. We train a convolutional neural network to classify and score the geometric outputs of the coarse-grained simulation to provide autonomous feedback for design optimization. We then construct a genetic algorithm that generates and selects large batches of material designs that compete with one another to evolve and converge on optimal objective-matching designs. We show that we are able to explore the large design space and learn important parameters and traits. We identify vital relationships between the material scale size and the range of shape change that can be achieved by individual domains and we elucidate trade-offs between different design parameters. Finally, we discover material designs capable of transforming into multiple different digits in different actuation states. © Kuan-Lin Chen and Rebecca Schulman.

关键词： Genetic algorithms

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Synergizing Machine Learning, Molecular Simulation and Experiment to Develop Polymer Membranes for Solvent Recovery

SSRN

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SSRN 2023年

作者： Jiang, Jianwen Xu, Qisong Gao, Jie Feng, Fan Chung, Tai-Shung Department of Chemical and Biomolecular Engineering National University of Singapore 117576 Singapore National Taiwan University of Science and Technology Taipei10607 Taiwan

Organic solvent nanofiltration (OSN) is a robust membrane technology for solvent recovery and molecular separation in harsh conditions. However, the current OSN membranes are largely synthesized through trial-and-error methods. In this study, machine learning (ML), molecular simulation (MS) and experiment are synergized for the development of high-performance OSN membranes. Using three different learning strategies, ML models are constructed to identify critical gross properties (i.e., solvent viscosity, membrane thickness and contact angle) and establish a phenomenological relationship for permeability prediction. Subsequently, ML models based on molecular representation via concatenated fragments are developed to predict solvent permeabilities in three polymer of intrinsic microporosity (PIM) membranes (PIM-A1, CX-PIM-A1 and PIM-8). Out of these PIMs, PIM-A1 is predicted to possess the highest methanol permeability. Furthermore, the PIM-A1 membrane is experimentally fabricated and found to exhibit nearly complete solute rejection and methanol permeability of 2.33 × 10-6 L·m/m2·h·bar, which is higher than that of archetypal PIM-1. This study demonstrates the synergy of ML, MS and experiment can fundamentally elucidate and quantitatively predict solvent permeation in polymer membranes, and the holistic approach may advance the development of new membranes for solvent recovery and other important separation processes. © 2023, The Authors. All rights reserved.

关键词： Nanofiltration

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Machine Learning for Identifying Grain Boundaries in Scanning Electron Microscopy (SEM) Images of Nanoparticle Superlattices

arXiv

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arXiv 2025年

作者： Paruchuri, Aanish Thrasher, Carl Hart, A.J. Macfarlane, Robert Jayaraman, Arthi Science in Data Science Program University of Delaware NewarkDE19713 United States Department of Materials Science and Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Department of Mechanical Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Department of Chemical and Biomolecular Engineering University of Delaware 150 Academy St NewarkDE19713 United States Department of Materials Science and Engineering University of Delaware NewarkDE19713 United States Data Science Institute University of Delaware NewarkDE19713 United States

Nanoparticle superlattices consisting of ordered arrangements of nanoparticles exhibit unique optical, magnetic, and electronic properties arising from nanoparticle characteristics as well as their collective behaviors. Understanding how processing conditions influence the nanoscale arrangement and microstructure is critical for engineering materials with desired macroscopic properties. Microstructural features such as grain boundaries, lattice defects, and pores significantly affect these properties but are challenging to quantify using traditional manual analyses as they are labor-intensive and prone to errors. In this work, we present a machine learning workflow for automating grain segmentation in scanning electron microscopy (SEM) images of nanoparticle superlattices. This workflow integrates signal processing techniques, such as Radon transforms, with unsupervised learning methods like agglomerative hierarchical clustering to identify and segment grains without requiring manually annotated data. In the workflow we transform the raw pixel data into explainable numerical representation of superlattice orientations for clustering. Benchmarking results demonstrate the workflow's robustness against noisy images and edge cases, with a processing speed of four images per minute on standard computational hardware. This efficiency makes the workflow scalable to large datasets and makes it a valuable tool for integrating data-driven models into decision-making processes for material design and analysis. For example, one can use this workflow to quantify grain size distributions at varying processing conditions like temperature and pressure and using that knowledge adjust processing conditions to achieve desired superlattice orientations and grain sizes. © 2025, CC BY.

关键词： Grain boundaries

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Reachability Analysis of Nonlinear Discrete-Time Systems Using Polyhedral Relaxations and Constrained Zonotopes*

Reachability Analysis of Nonlinear Discrete-Time Systems Usi...

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IEEE Conference on Decision and Control

作者： Brenner S. Rego Guilherme V. Raffo Marco H. Terra Joseph K. Scott Department of Chemical and Biomolecular Engineering Georgia Institute of Technology 311 Ferst Dr. Atlanta GA USA Department of Electrical and Computer Engineering University of Säo Paulo Säo Carlos SP Brazil Department of Electronics Engineering and the Graduate Program in Electrical Engineering Federal University of Minas Gerais Belo Horizonte MG Brazil

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This paper presents a novel algorithm for reachability analysis of nonlinear discrete-time systems. The proposed method combines constrained zonotopes (CZs) with polyhedral relaxations of factorable representations of nonlinear functions to propagate CZs through nonlinear functions, which is normally done using conservative linearization techniques. The new propagation method provides better approximations than those resulting from linearization procedures, leading to significant improvements in the computation of reachable sets in comparison to other CZ methods from the literature. Numerical examples highlight the advantages of the proposed algorithm.

关键词： Discrete-time systems Linearization techniques Approximation algorithms Complexity theory Reachability analysis

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