Results 211 to 220 of about 53,002 (284)

Research on Temperature Control Method of Rice Noodles Extruder Based on APSO-MPC. [PDF]

Sensors (Basel)
Zhang M, Yang Y, Gao G, Li Z, He Y, Li P, Lv H.   +6 more
europepmc   +1 more source

Challenges and enablers in fluidization technology

AIChE Journal, EarlyView.
Abstract Gas–solid fluidized beds provide excellent heat and mass transfer for high‐throughput operations from coating to catalytic conversion and underpin emerging low‐carbon technologies. Yet industrial reliability, scale‐up, and control lag scientific understanding, particularly as finer, stickier, and more variable feedstocks increasingly challenge
J. Ruud van Ommen, Jia Wei Chew
wiley   +1 more source

Transforming Adsorption-Energy Linear Correlations via Rescaling and Segmentation. [PDF]

ACS Catal
Azcona-Aliende N, Rodriguez P, Calle-Vallejo F.   +2 more
europepmc   +1 more source

Optimizing syngas production for enhanced gas turbine power generation: A thermodynamic and feedstock analysis

Next Energy
Toyese Oyegoke, Abdullahi Jibrin
openaire   +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

Liquid-Liquid and Vapor-Liquid-Liquid Equilibria of the Alkyl Palmitate + Alkyl-OH + Glycerol Systems at 101.3 kPa-Measurements, Quality Test/Consistency, Thermodynamic Modeling and Molecular Dynamics Simulations. [PDF]

Molecules
Carvalho F, Pena M, Silveira M, Freire N, Guimarães D, Biswas R, Arce P.   +6 more
europepmc   +1 more source

Topology‐Aware Machine Learning for High‐Throughput Screening of MOFs in C8 Aromatic Separation

Advanced Intelligent Discovery, EarlyView.
We screened 15,335 Computation‐Ready, Experimental Metal–Organic Frameworks (CoRE‐MOFs) using a topology‐aware machine learning (ML) model that integrates structural, chemical, pore‐size, and topological descriptors. Top‐performing MOFs exhibit aromatic‐enriched cavities and open metal sites that enable π–π and C–H···π interactions, serving as ...
Yu Li, Honglin Li, Jialu Li, Wan‐Lu Li
wiley   +1 more source

Computational approaches in chemical space exploration for carbon fixation pathways. [PDF]

NPJ Syst Biol Appl
Abel AS, Lauber N, Andersen JL, Fagerberg R, Merkle D, Flamm C.   +5 more
europepmc   +1 more source

Deep Learning‐Assisted Design of Mechanical Metamaterials

Advanced Intelligent Discovery, EarlyView.
This review examines the role of data‐driven deep learning methodologies in advancing mechanical metamaterial design, focusing on the specific methodologies, applications, challenges, and outlooks of this field. Mechanical metamaterials (MMs), characterized by their extraordinary mechanical behaviors derived from architected microstructures, have ...
Zisheng Zong, Pu Wen, Zhiping Chai, Dunxuan Wu, Han Ding, Zhigang Wu   +5 more
wiley   +1 more source

Asymptotic quantification of entanglement with a single copy. [PDF]

Nat Phys
Lami L, Berta M, Regula B.
europepmc   +1 more source