Results 161 to 170 of about 2,417 (257)

Identification and verification of feature biomarkers associated with CD14⁺ monocytes in type 1 diabetes. [PDF]

J Diabetes Investig
Huang J, Ding Y, Yu T, Chen S, He J, Shi L, Wang X.   +6 more
europepmc   +1 more source

Spatial‐Compatibility‐Assisted Molecular Intercalation in MXenes

Advanced Materials, EarlyView.
This work demonstrates a solvent–free NH4F–mediated route for simultaneous Al removal and molecular intercalation in MXenes. Intercalants are selected by comparing their crystallographic X, Y, and Z dimensions with the MXene interlayer spacing, establishing a spatial‐compatibility criterion that rationalizes guest–host matching and enables predictive ...
Minhao Sheng, Xiaoqing Bin, Xiangyang Liu, Yingji Zhao, Wenxiu Que, Yusuke Asakura, Yusuke Yamauchi   +6 more
wiley   +1 more source

Real-time multiple people gait recognition in the edge. [PDF]

Sci Rep
Ruiz-Barroso P, González-Linares JM, Castro FM, Guil N.   +3 more
europepmc   +1 more source

Toward Pore‐Engineered 3D‐Printed Materials for Sorption Water Harvesting, Interfacial Evaporation, and Radiative Cooling

Advanced Materials, EarlyView.
Water harvesting, radiative cooling, and interfacial solar evaporation are fundamentally governed by coupled heat, mass, and light transport processes. These processes are mediated by pore architecture, including pore size, connectivity, and hierarchical organization.
Dejan J. Trajkovski, Higgins M. Wilson, Abdallah Y. M. Ali, Jiaxing Xu, Ruzhu Wang, Primož Poredoš   +5 more
wiley   +1 more source

Predicting offer burden to optimize batch sizes in simultaneously expiring kidney offers. [PDF]

Front Artif Intell
Berry S, Görgülü B, Tunç S, Cevik M.
europepmc   +1 more source

Descriptors to Dynamics: A Materials and Device Perspective on in‐Materio Physical Reservoir Computing for Neuromorphic Edge Intelligence

Advanced Materials, EarlyView.
Intrinsic material dynamics are harnessed as computational resources for neuromorphic in‐materio physical reservoir computing. Defects, ionic motion, interfaces, percolation, geometry, and biasing shape transient states that provide fading memory, nonlinearity, and high‐dimensional projection for simple readout. A descriptor‐to‐dynamics framework links
Kshitij RB Singh, Alif Syafiq Kamarol Zaman, Muzhen Xu, Yuki Usami, Saptarshi Das, Hirofumi Tanaka   +5 more
wiley   +1 more source

Dynamic Electrophoretic Assembly Coupled With Water‐Splitting‐Induced pH Control Enables Precision Ion Sieving Membranes

Advanced Materials, EarlyView.
A green, external‐catalyst‐free route grows defect‐free covalent organic framework membranes directly on ion‐exchange supports using an alternating electric field and in situ‐generated acid. Through a self‐regulating, self‐healing mechanism, the membranes achieve near‐complete, ultra‐fast separation of monovalent ions such as Li+, Na+, and K+, offering
Mohammad Hossein Jandaghian, Petric Marc Ruya, Ipek Ozyurt, Ali Atyabi, Samuel Eyley, Wim Thielemans, Alexander Volodin, Riccardo Alessandri, Xing Yang   +8 more
wiley   +1 more source

Towards sustainable AI knowledge-base assistants in computer science education: on-premise deployment and optimization with open educational resources. [PDF]

Front Psychol
Shen X, Feng L, Hua S, Liu D, Xie Z, Liu B.   +5 more
europepmc   +1 more source

Spectrally Coordinated Plasmonic–Photothermal Photocatalysts for Mineralization of Mixed Indoor VOCs Under White LED Illumination

Advanced Materials, EarlyView.
A plasmonic–photothermal–photocatalytic hybrid comprising Cu(II)‐grafted TiO2, yolk–shell Au@SiO2, and reduced graphene oxide sheets (CuT/AuSi‐GS) harvests full‐spectrum white light‐emitting diode (LED) light via complementary photocatalytic oxidation, localized surface‐plasmon‐resonance‐induced electric‐field enhancement, and photothermal acceleration.
Minhyung Lee, Geun Young Kim, Gahye Shin, Haein Cho, Juwon Lee, Jinglin Wen, Wooyul Kim, Changha Lee, Kangwoo Cho, Jae‐Hyuk Kim, Hyoung‐il Kim   +10 more
wiley   +1 more source

Enhancing IIoT security through blockchain-enabled workload analysis in fog computing environments. [PDF]

Sci Rep
Samriya JK, Kumar A, Bhansali A, Malik M, Pan SH, Arya V, Alhalabi W, Gupta BB.   +7 more
europepmc   +1 more source