Results 71 to 80 of about 225,085 (221)
A neural network‐enabled permittivity engineering paradigm is introduced, transcending traditional trial‐and‐error design. By decoupling electromagnetic parameters and screening a high‐throughput feature space, an ultrathin (1.0 mm) magnetic absorber is inversely designed, experimentally achieving a superior and customizable 5.1 GHz bandwidth and ...
Chenxi Liu +9 more
wiley +1 more source
ARTP mutagenesis yielded Saccharopolyspora spinosa mutant D184 with improved extracellular nitrogen utilization. An integrated workflow of protease genetic manipulation, multi‐omics, and rational synergy design pinpointed a pepP‐clpP‐htpX synergistic triangular combination.
Duo Jin +9 more
wiley +1 more source
This work demonstrates a novel cascade photocatalysis concept using Fe single‐atom catalysts (Fe@C3N4 SAC) to directly upcycle plastics (PET, PP, PE, PVC) into valuable acetic acid at ambient conditions. Inspired by microbial degradation, the bifunctional cascade photocatalyst combines Fenton‐like oxidation and CO2 photoreduction, as validated by ...
Wei Wei +21 more
wiley +1 more source
This study introduces a multidimensional framework integrating electrical performance, cost, and life cycle assessment for 140 real and virtual battery cells. Results show that LFP offers low emissions and costs, sodium‐ion excels in resource efficiency, and pouch housings and higher energy densities effectively reduce environmental burdens.
Nicolas Peter Kaiser +7 more
wiley +1 more source
This study proposes a method to increase the value of solar power in balancing markets by managing prediction errors. The approach models prediction uncertainties and quantifies reserve requirements based on a probabilistic model. This enables the more reliable participation of photovoltaic plants in balancing markets across multiple sites, especially ...
Jindan Cui +3 more
wiley +1 more source
In this study we employed support vector regressor and quantum support vector regressor to predict the hydrogen storage capacity of metal–organic frameworks using structural and physicochemical descriptors. This study presents a comparative analysis of classical support vector regression (SVR) and quantum support vector regression (QSVR) in predicting ...
Chandra Chowdhury
wiley +1 more source
Designing Memristive Materials for Artificial Dynamic Intelligence
Key characteristics required of memristors for realizing next‐generation computing, along with modeling approaches employed to analyze their underlying mechanisms. These modeling techniques span from the atomic scale to the array scale and cover temporal scales ranging from picoseconds to microseconds. Hardware architectures inspired by neural networks
Youngmin Kim, Ho Won Jang
wiley +1 more source
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley +1 more source
This work presents a novel generative artificial intelligence (AI) framework for inverse alloy design through operations (optimization and diffusion) within learned compact latent space from variational autoencoder (VAE). The proposed work addresses challenges of limited data, nonuniqueness solutions, and high‐dimensional spaces.
Mohammad Abu‐Mualla +4 more
wiley +1 more source
This work introduces a novel framework for identifying non‐small cell lung cancer biomarkers from hundreds of volatile organic compounds in breath, analyzed via gas chromatography‐mass spectrometry. This method integrates generative data augmentation and multi‐view feature selection, providing a stable and accurate solution for biomarker discovery in ...
Guancheng Ren +10 more
wiley +1 more source

