Results 201 to 210 of about 1,259,133 (314)

Machine learning potentials for modeling alloys across compositions. [PDF]

Sci Adv
Sheriff K, Xiao DZ, Cao Y, Owen LR, Freitas R.   +4 more
europepmc   +1 more source

Embedded Ferroelectric Nanoclusters Can Drive Polarization Reversal in a Non‐Ferroelectric Polar Film via the Proximity Effect

Advanced Functional Materials, EarlyView.
Ferroelectric nanoclusters create local internal fields in a normally non‐switchable polar film because of polarization mismatch at their interfaces. That field opposes polarization in the regions with larger polarization and reinforces polarization in the regions with smaller polarization.
Anna N. Morozovska, Eugene A. Eliseev, Sergei V. Kalinin, Long‐Qing Chen, Dean R. Evans, Venkatraman Gopalan   +5 more
wiley   +1 more source

Tuning Redox Dynamics in Cu‐Based Electrocatalysts: Effect of Secondary Metals

Advanced Functional Materials, EarlyView.
Advanced analysis of operando X‐ray absorption spectroscopy data reveals that secondary metals affect the redox processes in Cu‐based catalysts for electrocatalytic CO2 reduction. This influences the amount of oxides formed under pulsed reaction conditions and the distribution of reaction products.
Martina Rüscher, Andrea Martini, Eduardo Ortega, Lichen Bai, Eric Liberra, Daniel Escalera‐López, Hyo Sang Jeon, Antonia Herzog, Clara Rettenmaier, Dorottya Hursán, David Koshy, Mariana Cecilio de Oliveira Monteiro, Timon Wagner, Leon Jacobse, Felix Haase, Arno Bergmann, Janis Timoshenko, Beatriz Roldan Cuenya   +17 more
wiley   +1 more source

Survival prediction of colorectal cancer using 101 machine learning methods based on immune-related genes: A machine learning study. [PDF]

Medicine (Baltimore)
Liu B, Li H, Hao M, Liu X, Yuan D, Huang W, Li W, Zeng J, Zhu Y, Ding L.   +9 more
europepmc   +1 more source

Mechanistic Control of C─H Bond Activation and C─C Coupling in Light‐Driven Methane Conversion to C2+ Products over Multifunctional Photocatalysts

Advanced Functional Materials, EarlyView.
Methane photocatalytic activation under mild conditions remains a formidable challenge in the synthesis of solar fuels. In this review, we emphasize the growing importance of multifunctional materials and hybrid systems within a unified mechanistic framework that integrates selective C─H bond activation, formation and control of intermediates, C─C ...
Di Hu, Yuheng Jiang, Vitaly V. Ordomsky, Bastian Mei, Zhiyong Tang, Andrei Y. Khodakov   +5 more
wiley   +1 more source

Machine Learning Approaches for Compound-Target Interaction Prediction: A Review. [PDF]

Foods
Zhang J, Li T, Yan C, Li Y, Yu Y, Wang J, Sun B.   +6 more
europepmc   +1 more source

High‐Throughput Digital Decoding of Vascular Heterogeneity in Patient‐Specific Tumor Microenvironments

Advanced Healthcare Materials, EarlyView.
ABSTRACT Quantitative characterization of vascular heterogeneity in complex microphysiological systems (MPS), particularly within patient‐derived tumor microenvironments, remains a major challenge for scalable disease modeling and therapeutic evaluation.
Jungseub Lee, Wooju Park, Sujin Hyung, Sangmin Jung, Kyungho Lee, Junseok Jeon, Noo Li Jeon, Jeeyun Lee, Hye Ryoun Jang, Jihoon Ko   +9 more
wiley   +1 more source

Progress in Machine Learning-Assisted Biosensors for Alzheimer's Disease. [PDF]

Biosensors (Basel)
Feng Y, Chen C.
europepmc   +1 more source

Engineering Nanoplatforms for Alzheimer's Disease Detection via Biomolecular Corona Proteomic and Lipidomic Profiling

Advanced Healthcare Materials, EarlyView.
Engineered nanoparticles capture disease‐specific biomolecular coronas that uncover hidden molecular features of Alzheimer's disease. Combined proteomic and lipidomic analyses reveal a characteristic shift in ribosomal machinery and energy metabolism, generating a multiomic fingerprint that supports accurate disease detection and opens new ...
Antonietta Greco, Roberto Baretta, Andrea Di Fonzo, Andrea Armirotti, Stefano Cinti, Mara Mirasoli, Marco Frasconi, Claudia Corbo   +7 more
wiley   +1 more source

Machine learning reveals microbiome differences by periodontitis severity. [PDF]

PLoS One
Seo SH, Lee JW, Oh S, Hong JS, Lee BS, Kwon SJ, Kim KS, Park JS, Heo JS, Ahn KH, Lee HJ, Park KU.   +11 more
europepmc   +1 more source