Results 141 to 150 of about 746,427 (311)

Label‐Free and Low‐Power Driven Cancer Biomarker Detection Enabled by 2D Hexagonal Titanium Oxide

Advanced Functional Materials, EarlyView.
A low‐power driven FET biosensor based on 2D hexagonal TiO2 detects the cancer biomarker carcinoembryonic antigen with high sensitivity, a low detection limit of 0.22 pg mL−1, and excellent selectivity. Leveraging the unique electronic properties of the material, this work demonstrates strong potential for integration into miniature and portable cancer
Yange Luan, Qijie Ma, Yihong Hu, Xinyi Hu, Guanyu Chen, Jiaru Zhang, Zhong Li, Dongdong Xie, Guanghui Ren, Yunyi Yang, Guifu Ding, Kate Fox, Bao Yue Zhang, Jian Zhen Ou   +13 more
wiley   +1 more source

Author Correction: Legendre based neural networks integrated with heuristic algorithms for the analysis of Lorenz chaotic model: an intelligent and comparative study. [PDF]

Sci Rep
Masood K, Arshad M, Abubakar M, Aslam MN, Alghamdi MA, Almotiri SH.   +5 more
europepmc   +1 more source

Enhanced Performance and In Situ TEM Investigation in High Entropy Alloy Electrode Based Memristors

Advanced Functional Materials, EarlyView.
This study utilizes in situ TEM, EDS, EELS, and APT to reveal switching in HEA/ZnO/Nb:STO RRAM. High diffusivity of Mn lowers the SET voltage to 1.5 V. Cr stabilizes the oxygen layer, while Fe, Co, and Ni stabilize the electrode. Based on these observations, the device achieves 30 ns switching, a 10⁷ memory window, and excellent endurance, being ...
Jing‐Yuan Tsai, Chun‐Wei Huang, Jui‐Yuan Chen, Che‐Hung Wang, Ping‐Hsien Wu, Jia‐Wei Chen, Yu‐Chen Yang, Tung‐huan Chou, Kun‐lin Lin, Ying‐Hao Chu, Wen‐Wei Wu   +10 more
wiley   +1 more source

Correction: A blood test-based machine learning model for predicting lung cancer risk. [PDF]

Front Med (Lausanne)
Schwartz L, Matania N, Levi M, Lazebnik T, Kushnir S, Yosef N, Hoogi A, Shlomi D.   +7 more
europepmc   +1 more source

Design and Applications of Multi‐Frequency Programmable Metamaterials for Adaptive Stealth

Advanced Functional Materials, EarlyView.
This article provides a comprehensive overview of metamaterials, including their fundamental principles, properties, synthesis techniques, and applications in stealth, as well as their challenges and future prospects. It covers topics that are more advanced than those typically discussed in existing review articles, while still being closely connected ...
Jonathan Tersur Orasugh, Ankita Mohanty, Amit Malakar, Suryasarathi Bose, Suprakas Sinha Ray   +4 more
wiley   +1 more source

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

IFIP TC13 International Conference on Human-Computer Interaction, 2015
L. Gamberini, A. Spagnolli, B. Blankertz, Samuel Kaski, J. Freeman, Laura Acqualagna, Oswald Barral, Maura Bellio, Luca Chech, M. Eugster, Eva Ferrari, Paolo Negri, V. Orso, Patrik Pluchino, Filippo Minelle, Baris Serim, M. Wenzel, Giulio Jacucci   +17 more
semanticscholar   +3 more sources

The fifth era of science: Artificial scientific intelligence. [PDF]

PLoS Biol
Miolane N.
europepmc   +1 more source

Smart, Bio‐Inspired Polymers and Bio‐Based Molecules Modified by Zwitterionic Motifs to Design Next‐Generation Materials for Medical Applications

Advanced Functional Materials, EarlyView.
Bio‐based and (semi‐)synthetic zwitterion‐modified novel materials and fully synthetic next‐generation alternatives show the importance of material design for different biomedical applications. The zwitterionic character affects the physiochemical behavior of the material and deepens the understanding of chemical interaction mechanisms within the ...
Theresa M. Lutz, Jonas De Breuck, Sahar Salehi, Meike N. Leiske   +3 more
wiley   +1 more source

Editorial: Methods in brain-computer interfaces: 2023. [PDF]

Front Hum Neurosci
Borra D, Ma M, Martinez-Martin E, Xia L.
europepmc   +1 more source

Unleashing the Power of Machine Learning in Nanomedicine Formulation Development

Advanced Functional Materials, EarlyView.
A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore, Cristiano Pesce, Antonietta Greco, Claudia Pisante, Greta Avancini, Valentina Di Francesco, Yosi Shamay, Paolo Decuzzi   +7 more
wiley   +1 more source