Machine Learning for Analyzing Atomic Force Microscopy (AFM) Images Generated from Polymer Blends DOI Creative Commons

Aanish Paruchuri,

Yunfei Wang, Xiaodan Gu

et al.

Digital Discovery, Journal Year: 2024, Volume and Issue: 3(12), P. 2533 - 2550

Published: Jan. 1, 2024

In this paper, we present a new machine learning (ML) workflow with unsupervised techniques to identify domains within atomic force microscopy (AFM) images obtained from polymer films.

Language: Английский

Review of progress in calculation and simulation of high-temperature oxidation DOI
Dongxin Gao, Zhao Shen, Kai Chen

et al.

Progress in Materials Science, Journal Year: 2024, Volume and Issue: 147, P. 101348 - 101348

Published: July 31, 2024

Language: Английский

Citations

61
Machine learning-assisted carbon dots synthesis and analysis: state of the art and future directions DOI
Fanyong Yan, Ruixue Bai, Juanru Huang

et al.

TrAC Trends in Analytical Chemistry, Journal Year: 2025, Volume and Issue: unknown, P. 118141 - 118141

Published: Jan. 1, 2025

Language: Английский

Citations

1
Rationally Designed High-Temperature Polymer Dielectrics for Capacitive Energy Storage: An Experimental and Computational Alliance DOI

Pritish S. Aklujkar,

Rishi Gurnani,

Pragati Rout

et al.

Progress in Polymer Science, Journal Year: 2025, Volume and Issue: unknown, P. 101931 - 101931

Published: Feb. 1, 2025

Language: Английский

Citations

1
Poly(phenylalanine) and poly(3,4-dihydroxy-L-phenylalanine): Promising biomedical materials for building stimuli-responsive nanocarriers DOI

Lingcong Zeng,

Dandan Kang,

Linglin Zhu

et al.

Journal of Controlled Release, Journal Year: 2024, Volume and Issue: 372, P. 810 - 828

Published: July 5, 2024

Language: Английский

Citations

4
Molecular separation applications of next-generation graphene oxide composite membranes with enhanced properties: Current status and future prospects DOI
Huan Li, Yang Lv, Zhishu Tang

et al.

Separation and Purification Technology, Journal Year: 2024, Volume and Issue: 358, P. 130451 - 130451

Published: Nov. 8, 2024

Language: Английский

Citations

4
Machine‐Learning‐Enhanced Trial‐and‐Error for Efficient Optimization of Rubber Composites DOI Open Access
Wei Deng, Lijun Liu, Xiaohang Li

et al.

Advanced Materials, Journal Year: 2025, Volume and Issue: unknown

Published: March 10, 2025

The traditional trial-and-error approach, although effective, is inefficient for optimizing rubber composites. latest developments in machine learning (ML)-assisted methodologies are also not suitable predicting and composite properties. This due to the dependency of properties on processing conditions, which prevents alignment data collected from different sources. In this work, a novel workflow called ML-enhanced approach proposed. integrates orthogonal experimental design with symbolic regression (SR) effectively extract empirical principles. combination enables optimization process retain characteristics while significantly improving efficiency capability. Using composites as model system, extracts principles encapsulated by high-frequency terms SR-derived mathematical formulas, offering clear guidance material property optimization. An online platform has been developed that allows no-code usage proposed methodology, designed seamlessly integrate into existing process.

Language: Английский

Citations

0
Advances in high‐throughput experiments of polymer crystallization for developing polymer processing DOI Creative Commons

Bao Deng,

Jian Wu, Hao Lin

et al.

Materials Genome Engineering Advances, Journal Year: 2025, Volume and Issue: unknown

Published: March 13, 2025

Abstract Polymer crystallization, an everlasting subject in polymeric materials, holds great significance not only as a fundamental theoretical issue but also pivotal basis for directing polymer processing. Given its multistep, rapid, and thermodynamic nature, tracing comprehending crystallization pose formidable challenge, particularly when it encounters practical processing scenarios that involve complex coupled fields (such temperature, flow, pressure). The advent of high‐time spatially resolved experiments paves the way situ investigations crystallization. In this review, we delve into strides studying under effects external via state‐of‐the‐art high‐throughput experiments. We highlight intricate setup these experimental devices, spanning from laboratory pilot levels to industrial level. individual combined on are discussed. By breaking away conventional “black box” research approach, special interest is paid crystalline behavior polymers during realistic Finally, underscore advancements outline promising development.

Language: Английский

Citations

0
Multi-Criteria and CNN Analysis of Al2O3/ TiO2/ Egg Shell/ ATH Ceramic Fillers in Glass Fiber-epoxy composites DOI Creative Commons

H. Mohit,

V.V. Vamsi Krishna,

Sanjay Mavinkere Rangappa

et al.

Journal of Materials Research and Technology, Journal Year: 2025, Volume and Issue: unknown

Published: March 1, 2025

Language: Английский

Citations

0
Advanced Materials for Biological Field‐Effect Transistors (Bio‐FETs) in Precision Healthcare and Biosensing DOI Creative Commons
Manoj Kumar Pandey, Manish Bhaiyya, Prakash Rewatkar

et al.

Advanced Healthcare Materials, Journal Year: 2025, Volume and Issue: unknown

Published: April 10, 2025

Abstract Biological Field Effect Transistors (Bio‐FETs) are redefining the standard of biosensing by enabling label‐free, real‐time, and extremely sensitive detection biomolecules. At center this innovation is fundamental empowering role advanced materials, such as graphene, molybdenum disulfide, carbon nanotubes, silicon. These when harnessed with downstream biomolecular probes like aptamers, antibodies, enzymes, allow Bio‐FETs to offer unrivaled sensitivity precision. This review an exposition how advancements in materials science have permitted detect biomarkers low concentrations, from femtomolar attomolar levels, ensuring device stability reliability. Specifically, examines incorporation cutting‐edge architectures, flexible / stretchable multiplexed designs, expanding frontiers contributing development more adaptable user‐friendly Bio‐FET platforms. A key focus placed on synergy artificial intelligence (AI), Internet Things (IoT), sustainable approaches fast‐tracking toward transition research into practical healthcare applications. The also explores current challenges material reproducibility, operational durability, cost‐effectiveness. It outlines targeted strategies address these hurdles facilitate scalable manufacturing. By emphasizing transformative played their cementing position Bio‐FETs, positions a cornerstone technology for future solution precision would lead era where herald massive strides biomedical diagnostics subsume.

Language: Английский

Citations

0
Predicting rigidity and connectivity percolation in disordered particulate networks using graph neural networks DOI Creative Commons
David Head

Physical review. E, Journal Year: 2025, Volume and Issue: 111(4)

Published: April 11, 2025

Graph neural networks can accurately predict the chemical properties of many molecular systems, but their suitability for large, macromolecular assemblies such as gels is unknown. Here, graph were trained and optimized two large-scale classification problems: rigidity a network, connectivity percolation status, which nontrivial to determine systems with periodic boundaries. Models on lattice found achieve accuracies >95% classification, slightly lower scores due inherent class imbalance in data. Dynamically generated off-lattice achieved consistently overall correlated nature network geometry that was absent lattices. An open source tool provided allowing usage highest-scoring models, directions future improved tools surmount challenges limiting accuracy certain situations are discussed. Published by American Physical Society 2025

Language: Английский

Citations

0