Results 181 to 190 of about 1,243,089 (305)

Artificial Intelligence-Enhanced Multi-Algorithm R Shiny Application for Predictive Modeling and Analytics: Case Study of Alzheimer Disease Diagnostics. [PDF]

JMIR Aging
Wenzheng H, Agyemang EF, Srivastav SK, Shaffer JG, Kakraba S.   +4 more
europepmc   +1 more source

Electrochemical Formation of BiVO4/BiPO4 Photoanodes for Enhanced Selectivity toward H2O2 Generation

Advanced Functional Materials, EarlyView.
In acidic KPi, V dissolves from the BiVO4 lattice, while adsorbed phosphate reacts with the electrode under an external bias, forming a BiPO4 surface layer. This BiPO4 layer exhibits stronger bicarbonate adsorption, redirecting the water oxidation pathway toward two‐electron H2O2 production.
Kaijian Zhu, Xinyi Zhang, Lan Wen, Hongjiang Chen, Yudhistira Tirtayasri Amrillah, Vivi Fauzia, Hongzhen Dong, Shaoqiang Su, Wenjun Luo, Ferry Anggoro Ardy Nugroho, Patrick H.‐L. Sit, Yun Hau Ng, Fatwa F. Abdi   +12 more
wiley   +1 more source

Unusual Swelling Behavior of Hydrogels Modified with Spiropyran as Appendage or Crosslinker

Advanced Functional Materials, EarlyView.
Not so innocent after all—spiropyran crosslinkers in methylenebisacrylamide‐crosslinked poly(acrylamide‐co‐acrylic acid) hydrogels increase crosslinking density, but also, counterintuitively, increase swelling. Charge complexation, cooperative chemo‐mechanical effects, and aggregation may explain these observations.
Michael M. Lerch, Ankita Shastri, Thomas B. H. Schroeder, Amos Meeks, Shucong Li, Anna V. Shneidman, Michael Aizenberg, Joanna Aizenberg   +7 more
wiley   +1 more source

Bimetallic Nanoreactor Activates cGAS‐STING Pathway via mtDNA Release for Cancer Metalloimmunotherapy

Advanced Functional Materials, EarlyView.
A bimetallic Mn–Ca nanoreactor (MCC) is developed as a non‐nucleotide STING nanoagonist for cancer metalloimmunotherapy. MCC induces Ca2+ overload and hydroxyl radical generation, resulting in mitochondrial damage and mtDNA release. The released mtDNA cooperates with Mn2+ to robustly activate cGAS–STING signaling.
Xin Wang Mo, Nguyen Thi Nguyen, LiLi Guo, Ngoc Man Phan, Hyunsu Shin, Tae‐il Kim, Yu Seok Youn, Jaeyun Kim   +7 more
wiley   +1 more source

From Light to Energy: Machine Learning Algorithms for Position and Energy Deposition Estimation in Scintillator-SiPM Detectors. [PDF]

Sensors (Basel)
Simhony Y, Segal A, Amrani O, Etzion E.
europepmc   +1 more source

Solvent‐Free Bonding Mechanisms and Microstructure Engineering in Dry Electrode Technology for Lithium‐Ion Batteries

Advanced Functional Materials, EarlyView.
Dry electrode technology revolutionizes battery manufacturing by eliminating toxic solvents and energy‐intensive drying. This work details two promising techniques: dry spray deposition and polymer fibrillation. How their unique solvent‐free bonding mechanisms create uniform microstructures for thicker, denser electrodes, boosting energy density and ...
Yuhao Liang, Hao Long, Zimo Huang, Ting He, Xueming Chen, Meng Li, Hao Chen, Shanqing Zhang   +7 more
wiley   +1 more source

Boosting

, 2019
Edward Chmiel, Andrew Murphy
  +4 more sources

Tree-based ensemble learning models for protein-protein interactions detection: a review and experimental evaluation. [PDF]

BioData Min
Taha K.
europepmc   +1 more source

Tuning the Electronic Structure and Spin State of Fe─N─C Catalysts Using an Axial Oxygen Ligand and Fe Clusters for High‐Efficiency Rechargeable Zinc–Air Batteries

Advanced Functional Materials, EarlyView.
A FeN4─O/Clu@NC‐0.1Ac catalyst containing atomically‐dispersed FeN4─O sites (medium‐spin Fe2+) and Fe clusters delivered a half‐wave potential of 0.89 V for ORR and an overpotential of 330 mV at 10 mA cm−2 for OER in 0.1 m KOH. When the catalyst was used in a rechargeable Zn–air battery, a power density of 284.5 mW cm−2 was achieved with excellent ...
Yongfang Zhou, Yu Mao, Mengyao Chang, Ziyun Wang, Wenke Xie, Cuizhu Ye, Shanghai Wei, Lars Thomsen, Geoffrey I. N. Waterhouse   +8 more
wiley   +1 more source

Machine learning prediction of germline <i>BRCA1/2</i> pathogenic variants in patients with ovarian cancer. [PDF]

BMJ Health Care Inform
Innella G, Erini G, De Leo A, Godino L, Caramanna L, Ferrari S, Miccoli S, Perrone AM, Zamagni C, De Iaco P, Turchetti D, Rucci P.   +11 more
europepmc   +1 more source