Results 161 to 170 of about 3,002,307 (310)

Selective Benzene Capture by Metal‐Organic Frameworks

Advanced Functional Materials, EarlyView.
Metal‐organic frameworks (MOFs) hold significant potential for capturing benzene from air emissions and hydrocarbon mixtures in liquid phases. This capability stems from their precisely engineered structures, versatile chemistries, and diverse binding interactions.
Zongsu Han, Ankit Mondal, Jiatong Huo, Yihao Yang, Hong‐Cai Zhou   +4 more
wiley   +1 more source

Quantum Dots and Perovskites‐Based Physically Unclonable Functions for Binary and Ternary Keys via Optical‐to‐Electrical Conversion

Advanced Functional Materials, EarlyView.
This study investigates optoelectronic PUFs that improve on traditional optical and electrical PUFs. The absorber materials are randomly coated through spray coating, ligand exchange, and dynamic spin coating. Incident light generates wavelength‐dependent binary multikey and enhances security ternary keys, approaching near‐ideal inter‐ and intra ...
Hanseok Seo, Taesung Park, Awais Ali, Byung Ku Jung, Young Kyun Choi, Junhyeok Park, Soong Ju Oh   +6 more
wiley   +1 more source

Machine learning meets human-computer interaction - introduction to the special issue [PDF]

, 1997

openalex   +1 more source

Printing Nacre‐Mimetic MXene‐Based E‐Textile Devices for Sensing and Breathing‐Pattern Recognition Using Machine Learning

Advanced Functional Materials, EarlyView.
This study presents a Ti3C2Tx MXene/WPU nacre‐mimetic nanomaterial as a printable ink for direct‐write printing onto textiles‐based sensors. The resulting wearable device demonstrates high sensitivity, biocompatibility, and mechanical strength. Furthermore, NFC‐enabled humidity sensor produces time‐series data, which informs a machine learning ...
Lulu Xu, Wenqing Chen, Qian Li, Rohit Gupta, Biswajoy Bagchi, Laurence B. Lovat, Manish K. Tiwari   +6 more
wiley   +1 more source

Postprocessing in machine learning and data mining [PDF]

, 2000
Ivan Brůha, A. Fazel Famili
openalex   +1 more source

Artificial Intelligence‐Driven Development in Rechargeable Battery Materials: Progress, Challenges, and Future Perspectives

Advanced Functional Materials, EarlyView.
AI is transforming the research paradigm of battery materials and reshaping the entire landscape of battery technology. This comprehensive review summarizes the cutting‐edge applications of AI in the advancement of battery materials, underscores the critical challenges faced in harnessing the full potential of AI, and proposes strategic guidance for ...
Qingyun Hu, Junyuan Lu, Jian Hui, Ziyuan Rao, Yang Ren, Hong Wang   +5 more
wiley   +1 more source

Learning in higher order Boltzmann machines using linear response [PDF]

, 2000
M.A.R. Leisink, Hilbert J. Kappen
openalex   +1 more source

Experimentally Validated Design Principles of D‐Block Transition Metal Single‐Atom Non‐Dissociative Chemisorption Solid‐State Hydrogen Storage Materials

Advanced Functional Materials, EarlyView.
A novel descriptor and a bottom‐up design principle are established to enable the rational design of hydrogen storage materials based on d‐block transition metal single‐atom COFs. By modulating H₂ adsorption through d‐orbital tuning, this approach achieves both high storage capacity and fast kinetics, while revealing a volcano‐type relationship between
Qiuyan Yue, Chao Li, Yong Gao, Zhenglong Li, Wen‐Gang Cui, Xinqiang Wang, Guoquan Na, Xiaowei Wang, Yiyang Wan, Mingchang Zhang, Yanxia Liu, Fulai Qi, Chenchen Li, Xu Xue, Jian Miao, Jing Zhang, Xiao Han, Pan Wang, Chang Guo, Xiacui Liu, Mingxia Gao, Wenping Sun, Jian Chen, Zhenhai Xia, Hongge Pan   +24 more
wiley   +1 more source

Machine learning in Alzheimer's disease genetics. [PDF]

Nat Commun
Bracher-Smith M, Melograna F, Ulm B, Bellenguez C, Grenier-Boley B, Duroux D, Nevado AJ, Holmans P, Tijms BM, Hulsman M, de Rojas I, Campos-Martin R, der Lee SV, Castillo A, Küçükali F, Peters O, Schneider A, Dichgans M, Rujescu D, Scherbaum N, Deckert J, Riedel-Heller S, Hausner L, Molina-Porcel L, Düzel E, Grimmer T, Wiltfang J, Heilmann-Heimbach S, Moebus S, Tegos T, Scarmeas N, Dols-Icardo O, Moreno F, Pérez-Tur J, Bullido MJ, Pastor P, Sánchez-Valle R, Álvarez V, Boada M, García-González P, Puerta R, Mir P, Real LM, Piñol-Ripoll G, García-Alberca JM, Rodriguez-Rodriguez E, Soininen H, Heikkinen S, de Mendonça A, Mehrabian S, Traykov L, Hort J, Vyhnalek M, Sandau N, Thomassen JQ, Pijnenburg YAL, Holstege H, van Swieten J, Ramakers I, Verhey F, Scheltens P, Graff C, Papenberg G, Giedraitis V, Williams J, Amouyel P, Boland A, Deleuze JF, Nicolas G, Dufouil C, Pasquier F, Hanon O, Debette S, Grünblatt E, Popp J, Ghidoni R, Galimberti D, Arosio B, Mecocci P, Solfrizzi V, Parnetti L, Squassina A, Tremolizzo L, Borroni B, Wagner M, Nacmias B, Spallazzi M, Seripa D, Rainero I, Daniele A, Piras F, Masullo C, Rossi G, Jessen F, Kehoe P, Magda T, Sánchez-Juan P, Sleegers K, Ingelsson M, Hiltunen M, Sims R, van der Flier W, Andreassen OA, Ruiz A, Ramirez A, EADB, Frikke-Schmidt R, Amin N, Roshchupkin G, Lambert JC, Van Steen K, van Duijn C, Escott-Price V.   +112 more
europepmc   +1 more source

Machine learning methods for Chinese web page categorization [PDF]

, 2000
Ji He, Ah‐Hwee Tan, Chew‐Lim Tan
openalex   +1 more source