Results 191 to 200 of about 34,853 (246)

A critical review of porous adsorbents for air separation: From fundamental insights to rational adsorbent design

AIChE Journal, EarlyView.
Abstract Air separation via selective adsorption using porous adsorbents offers energy‐efficient alternatives to cryogenic distillation for producing high‐purity O2 and N2. Adsorbent efficacy depends on balancing selectivity, durability, and performance consistency across varying conditions. This comprehensive review critically discusses the design and
Tianqi Wang, Daisong Chen, Tianyi Zhang, Boyu Zhang, Ruiqiang Wang, Zhengdong Liu, Mei Hong, Youssef Belmabkhout, Qinfen Gu, Jin Shang   +9 more
wiley   +1 more source

A Review of In Situ Quality Monitoring in Additive Manufacturing Using Acoustic Emission Technology. [PDF]

Sensors (Basel)
Chang W, Zhang Q, Chen W, Gao Y, Liu B, Li Z, Dang C.   +6 more
europepmc   +1 more source

Stochastic optimization of Power‐to‐Methanol: Production cost sensitivity to process design vs. scheduling

AIChE Journal, EarlyView.
Abstract Under time‐varying electricity prices, the production costs of Power‐to‐X processes with intermediate storage can be reduced by simultaneously optimizing the process unit design and size with their scheduling and operation. However, the production cost sensitivity to optimal process design or scheduling is unclear, especially when several ...
Simone Mucci, Dominik Bongartz
wiley   +1 more source

Extraction of driving behavior primitives considering driver expectation and vehicle dynamics. [PDF]

PLoS One
Ren Y, Cui X, Zheng X, Li X, Xi J.
europepmc   +1 more source

Exploring Quantum Support Vector Regression for Predicting Hydrogen Storage Capacity of Nanoporous Materials

Advanced Intelligent Discovery, EarlyView.
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

Feature Selection for Machine Learning‐Driven Accelerated Discovery and Optimization in Emerging Photovoltaics: A Review

Advanced Intelligent Discovery, EarlyView.
Feature selection combined with machine learning and high‐throughput experimentation enables efficient handling of high‐dimensional datasets in emerging photovoltaics. This approach accelerates material discovery, improves process optimization, and strengthens stability prediction, while overcoming challenges in data quality and model scalability to ...
Jiyun Zhang, Jiayi Tan, Qizhen Song, Tian Du, Jens Hauch, Christoph J. Brabec   +5 more
wiley   +1 more source

Research on LEACH Protocol Based on Dynamic Clustering and Routing Optimization. [PDF]

Sensors (Basel)
Wang T, Qu X, Cui H.
europepmc   +1 more source

Universally Accurate or Specifically Inadequate? Stress‐Testing General Purpose Machine Learning Interatomic Potentials

Advanced Intelligent Discovery, EarlyView.
We investigate MACE‐MP‐0 and M3GNet, two general‐purpose machine learning potentials, in materials discovery and find that both generally yield reliable predictions. At the same time, both potentials show a bias towards overstabilizing high energy metastable states. We deduce a metric to quantify when these potentials are safe to use.
Konstantin S. Jakob, Karsten Reuter, Johannes T. Margraf   +2 more
wiley   +1 more source

Development of a consensus molecular classifier for pancreatic ductal adenocarcinoma. [PDF]

Genome Med
Villoslada-Blanco P, Alonso L, Sabroso-Lasa S, Maquedano M, Estudillo L, Real FX, López de Maturana E, Malats N.   +7 more
europepmc   +1 more source

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

Advanced Intelligent Discovery, EarlyView.
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