Results 171 to 180 of about 3,041,404 (222)

Characterizing Biopolymer Electrolytes in Zinc–Air Batteries: Challenges, Best Practices, and a Robust Workflow Guiding Future Research Paths

Advanced Energy Materials, EarlyView.
Bio‐based gel polymer electrolytes promise sustainable, mechanically adaptable zinc–air batteries, yet their progress is constrained by inconsistent characterization. This review critically links formulation, structure, interfaces, and cell performance, identifies methodological gaps under alkaline operating conditions, and proposes application ...
Matteo Milanesi, Hiba Ali, Barbara Belza, Mattia F. Palermo, Claudio Gerbaldi, Giuseppe Antonio Elia, Nagore Ortiz‐Vitoriano   +6 more
wiley   +1 more source

Biophysical modeling of anatomically realistic prenatal cortical folding development. [PDF]

Nat Commun
Hou J, Wu Z, Jiang K, Wu T, Zhang L, Zhu D, Gao W, Razavi MJ, Liu T, Kuhl E, Li G, Wang X.   +11 more
europepmc   +1 more source

Gaussian Process Regression–Neural Network Hybrid with Optimized Redundant Coordinates: A New Simple Yet Potent Tool for Scientist's Machine Learning Toolbox

Advanced Intelligent Discovery, EarlyView.
A machine learning method, opt‐GPRNN, is presented that combines the advantages of neural networks and kernel regressions. It is based on additive GPR in optimized redundant coordinates and allows building a representation of the target with a small number of terms while avoiding overfitting when the number of terms is larger than optimal.
Sergei Manzhos, Manabu Ihara
wiley   +1 more source

Hemp-Derived Cellulose/NiFe₂O₄ Biocomposite Hydrogels for Flexible Optoelectronics. [PDF]

ACS Omega
Yalçın O, Al-Dala Ali AMJ, Coşkun R, Uslu H.   +3 more
europepmc   +1 more source

A Breadth‐First Pruned‐Enriched Rosenbluth Method for Force–Extension Simulations of Confined Semiflexible Chains

Advanced Intelligent Discovery, EarlyView.
The behaviors of semiflexible polymers such as DNA and protein are often reshaped by coupled interactions. Monte Carlo simulations assist in studying these systems. This work recasts the traditional chain‐growth strategy into a new framework: a fixed number of chains grow synchronously, while less relevant chains to the target system are removed and ...
Yihan Zhao, Jizeng Wang
wiley   +1 more source

AI‐BioMech: Deep Learning Prediction of Mechanical Behavior in Aperiodic Biological Cellular Materials

Advanced Intelligent Discovery, EarlyView.
AI‐BioMech is a deep learning framework that predicts the mechanical behavior of biological cellular materials directly from 2D images. By replacing traditional finite element analysis with semantic segmentation, it identifies stress and strain distributions with 99% accuracy, offering a high‐speed, scalable alternative for analyzing complex, aperiodic
Haleema Sadia, Marcelo A. Dias, Parvez Alam   +2 more
wiley   +1 more source

Deep-Learning System for Automatic Measurement of the Femorotibial Rotational Angle on Lower-Extremity Computed Tomography. [PDF]

J Imaging Inform Med
Lee SW, Lee GP, Yoon I, Kim YJ, Kim KG.
europepmc   +1 more source

In Situ Contact Angle Measurement for Autonomous Spin Coating in Self‐Driving Labs

Advanced Intelligent Discovery, EarlyView.
A vision‐based add‐on transforms commercial spin coaters into autonomous modules of Self‐Driving Labs. Combining a width‐scaled U‐Net with classical geometric analysis, the system simultaneously measures contact angles and estimates substrate pose using a single camera.
Sven Fischer, Micha Hiegle, Holger Röhm
wiley   +1 more source

Sensor-Based Differential Flatness-Based Trajectory Tracking Method and Its Application to Wheeled Mobile Robot Control. [PDF]

Sensors (Basel)
Krasavin A, Nazenova G, Dairbekova A, Kadyroldina A, Haidegger T, Alontseva D.   +5 more
europepmc   +1 more source

Particle Swarm Optimization as a Noise‐Tolerant Strategy for High‐Entropy Alloy Research

Advanced Intelligent Discovery, EarlyView.
A customized particle swarm optimization algorithm is benchmarked against batch Bayesian optimization across four high‐entropy alloy oxygen‐reduction landscapes. While both methods locate the global optimum comparably under noise‐free conditions, Bayesian optimization’s surrogate model tends to overfit noisy data.
Ahmad Tirmidzi, Matthias Arenz, Vladislav A. Mints   +2 more
wiley   +1 more source