Results 171 to 180 of about 98,687 (237)

Ferroelectric Quantum Dots for Retinomorphic In‐Sensor Computing

Advanced Materials, EarlyView.
This work has provided a protocol for fabricating retinomorphic phototransistors by integrating ferroelectric ligands with quantum dots. The resulting device combines ferroelectricity, optical responsiveness, and low‐power operation to enable adaptive signal amplification and high recognition accuracy under low‐light conditions, while supporting ...
Tingyu Long, Huanyu Zhou, Jaewan Ko, Hongwei Tan, Jaemin Lim, Yanfei Zhao, Daehan Kang, Eojin Yoon, Gyeong‐Tak Go, Somin Kim, Seung‐Woo Lee, Chan‐Yul Park, Hyojun Choi, Hyeran Kim, Hyung Joong Yun, Sung Hyuk Park, Kwan Sik Park, Jeong Woo Park, Mungeun Kim, Yong Soo Cho, Ho Won Jang, Wenqiang Yang, Min Hyuk Park, Wan Ki Bae, Sebastiaan van Dijken, Joona Bang, Tae‐Woo Lee   +26 more
wiley   +1 more source

Bioinspired Adaptive Sensors: A Review on Current Developments in Theory and Application

Advanced Materials, EarlyView.
This review comprehensively summarizes the recent progress in the design and fabrication of sensory‐adaptation‐inspired devices and highlights their valuable applications in electronic skin, wearable electronics, and machine vision. The existing challenges and future directions are addressed in aspects such as device performance optimization ...
Guodong Gong, You Zhou, Qingxiu Li, Wenyu Zhao, Sunyingyue Geng, Hangfei Li, Yan‐Bing Leng, Shirui Zhu, Guanglong Ding, Yongbiao Zhai, Ziyu Lv, Ye Zhou, Su‐Ting Han   +12 more
wiley   +1 more source

Artificial Intelligence‐Assisted Workflow for Transmission Electron Microscopy: From Data Analysis Automation to Materials Knowledge Unveiling

Advanced Materials, EarlyView.
AI‐Assisted Workflow for (Scanning) Transmission Electron Microscopy: From Data Analysis Automation to Materials Knowledge Unveiling. Abstract (Scanning) transmission electron microscopy ((S)TEM) has significantly advanced materials science but faces challenges in correlating precise atomic structure information with the functional properties of ...
Marc Botifoll, Ivan Pinto‐Huguet, Enzo Rotunno, Thomas Galvani, Catalina Coll, Payam Habibzadeh Kavkani, Maria Chiara Spadaro, Yann‐Michel Niquet, Martin Børstad Eriksen, Sara Martí‐Sánchez, Georgios Katsaros, Giordano Scappucci, Peter Krogstrup, Giovanni Isella, Andreu Cabot, Gonzalo Merino, Pablo Ordejón, Stephan Roche, Vincenzo Grillo, Jordi Arbiol   +19 more
wiley   +1 more source

Engineered Protein‐Based Ionic Conductors for Sustainable Energy Storage Applications

Advanced Materials, EarlyView.
Rational incorporation of charged residues into an engineered, self‐assembling protein scaffold yields solid‐state protein films with outstanding ionic conductivity. Salt‐doping further enhances conductivity, an effect amplified in the engineered variants. These properties enable the material integration into an efficient supercapacitor.
Juan David Cortés‐Ossa, Paolo Blesio, Marcial Fernandez‐Castro, Lisa Almonte, Maxence Fernandez, Mantas Liutkus, Perumal Pandurangan, Carlos Sabater, Aitor Villaverde, Manuel Melle‐Franco, Nurit Ashkenasy, Felipe Jiménez‐Ángeles, M. Carmen Morant‐Miñana, M. Reyes Calvo, Aitziber L. Cortajarena   +14 more
wiley   +1 more source

Mesoporous Silica Nanoparticles in Biomedicine: Advances and Prospects

Advanced Materials, EarlyView.
Mesoporous silica nanoparticles offer unique properties like high surface area, tunable pores, and functionalization. They excel in drug delivery, tissue engineering, and stimuli‐responsive therapies, enabling targeted and controlled treatments. With roles in cancer therapy and diagnostics, their clinical translation requires addressing challenges in ...
Miguel Manzano, María Vallet‐Regí
wiley   +1 more source

Meeting Event Recognition using a Parallel Recurrent Neural Net Approach [PDF]

, 2004
openaire  

Label‐Free SERS Fingerprinting of Neuroprotein Conformational Dynamics in Human Saliva

Advanced Materials, EarlyView.
Galvanic molecular entrapment (GME) is a label‐free method for detecting and quantifying neuroprotein conformational states. This technique enables direct surface binding and in situ hotspot generation around molecules, effectively overcoming challenges related to target localization and mismatched hotspot geometries.
Muhammad Shalahuddin Al Ja'farawy, Jun‐Yeong Yang, Chaewon Mun, Seunghun Lee, Chul Bum Cho, Yoo Hyun Um, Min‐Young Lee, Sung‐Gyu Park, Dong‐Ho Kim, Seung Ho Yang, Ho Sang Jung   +10 more
wiley   +1 more source

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Univariate Time Series Using Recurrent Neural Nets

, 2021
This chapter covers the basics of deep learning. First, it introduces the activation function, the loss function, and artificial neural network optimizers. Second, it discusses the sequence data problem and how a recurrent neural network (RNN) solves it. Third, the chapter presents a way of designing, developing, and testing the most popular RNN, which
Tshepo Chris Nokeri
semanticscholar   +2 more sources

Modelling Landsurface Time-Series with Recurrent Neural Nets

IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, 2018
Machine learning tools and semi-empirical models have been very successful in describing and predicting instantaneous climatic influences on the spatial and seasonal variability of biosphere state and function.
M. Reichstein, S. Besnard, N. Carvalhais, F. Gans, M. Jung, B. Kraft, M. Mahecha   +6 more
semanticscholar   +3 more sources

Recurrent neural nets achieving MLSE performance in bandlimited optical channels

2020 IEEE Photonics Conference (IPC), 2020
We explore long short-term memory-recurrent neural networks (LSTM-RNN) for mitigating severe inter-symbol-interference caused by bandlimited components.
Sai Chandra Kumari Kalla, Leslie Ann Rusch   +1 more
semanticscholar   +2 more sources