Results 201 to 210 of about 53,002 (284)

Thermomechanical load in a nonlocal rotating magneto-thermoelastic orthotropic material with Green Naghdi-III model. [PDF]

Sci Rep
Salah DM, Abd-Alla AM, El-Kabeir SMM, Bayones FS.   +3 more
europepmc   +1 more source

Asymmetric Multi‐Site Ion Exchange in Porous Carbon Electrodes

Advanced Energy Materials, EarlyView.
A lognormal‐distribution 2D EXSY NMR framework uncovers the full spectrum of ion dynamics in hierarchical porous carbons, bridging fast surface exchange and confined in‐pore transport—establishing structure–transport relationships for next‐generation energy storage, gas storage, and ion removal materials.
Henry R. N. B. Enninful, Marius Hermesdorf, Muhammad Abdullah Khan, Desirée Leistenschneider, Martin Oschatz, Rustem Valiullin   +5 more
wiley   +1 more source

Machine learning analysis of oral solid dosage formulation solubility variations by adjusting pressure and temperature. [PDF]

Sci Rep
Lahiq AA, Alshehri AA, Alsharif ST.
europepmc   +1 more source

Strain‐Directed Ru Redistribution to Form RuO2/Pt Mosaic Heterointerfaces for Acid‐Stable Water Oxidation

Advanced Energy Materials, EarlyView.
Strain‐driven Ru inward migration creates confined RuO2/Pt mosaic heterointerfaces within a PtNi multiframe. These interfaces enable redox‐asymmetric Pt–Ru coupling that stabilizes Ru4+, suppresses lattice oxygen participation, and promotes highly durable acidic oxygen evolution.
Yeji Park, Doyeop Kim, Jungho Kim, Ho Yeon Jang, Minseon Cha, Subeen Lee, Tae Kyung Lee, Taekyung Kim, Hionsuck Baik, Jinwon Choi, Taehyun Kwon, Seoin Back, Sung Jong Yoo, Kwangyeol Lee   +13 more
wiley   +1 more source

MXenes Surface Termination under Photoexcitation: Insights from Excited-State Pourbaix Diagrams. [PDF]

ACS Appl Mater Interfaces
Ontiveros D, Viñes F, Sousa C.
europepmc   +1 more source

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

Advanced Energy Materials, EarlyView.
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park, Saeed Pourasad, Jinhong Mun, Arunkumar Chitteth Rajan, Tae Hyeon Park, Anand Rohit, Mohammad Zafari, Tan‐Lien Pham, Eunjae Jung, Bonseung Koo, Miran Ha, Gi Beom Sim, Hyun Gyu Park, Won Woong Choi, Kwanghyeon Jo, Soohaeng Yoo Willow, David ChangMo Yang, Chang Woo Myung, Geunsik Lee, Kwang S. Kim   +19 more
wiley   +1 more source

Predicting copper leaching from slag: an interpretable machine learning approach under oxidative sulfuric acid conditions. [PDF]

RSC Adv
Kim SJ, Kang SS, Pae KN, Pak SI, Jo HI, Kim RJ.   +5 more
europepmc   +1 more source

Consolidation of multiple binary distillation columns for large heat duty savings

AIChE Journal, EarlyView.
ABSTRACT The enormous scales of chemical and petrochemical plants present significant challenges in separating and purifying numerous mixed streams generated within a facility, as well as in achieving effective energy utilization and process intensification.
Parikshit S. Kadu, Rakesh Agrawal
wiley   +1 more source

Marine diesel engine reliable intelligent fault diagnosis method based on the generalized multi-source information fusion. [PDF]

iScience
Xie Z, Mo C, Jia B.
europepmc   +1 more source

AI in chemical engineering: From promise to practice

AIChE Journal, EarlyView.
Abstract Artificial intelligence (AI) in chemical engineering has moved from promise to practice: physics‐aware (gray‐box) models are gaining traction, reinforcement learning complements model predictive control (MPC), and generative AI powers documentation, digitization, and safety workflows.
Jia Wei Chew, Ronnie Andersson, Thomas Bierweiler, Patrik Ryttestal, Torsten Wik   +4 more
wiley   +1 more source