Results 201 to 210 of about 594,656 (344)

MOFs and COFs in Electronics: Bridging the Gap between Intrinsic Properties and Measured Performance

Advanced Functional Materials, EarlyView.
Metal‐organic frameworks (MOFs) and covalent organic frameworks (COFs) hold promise for advanced electronics. However, discrepancies in reported electrical conductivities highlight the importance of measurement methodologies. This review explores intrinsic charge transport mechanisms and extrinsic factors influencing performance, and critically ...
Jonas F. Pöhls, R. Thomas Weitz
wiley   +1 more source

Sensitivity Improvement via Differential Detection for Frequency-Locking Diamond Magnetometers. [PDF]

Micromachines (Basel)
Zheng D, Gao J, Li Y, Wang H, Yang Y, Guo H, Wen H, Li Z, Tang J, Ma Z, Liu J.   +10 more
europepmc   +1 more source

CONSUMER’S “LOCK-IN” – A KEY ELEMENT OF THE SWITCHING COSTS IN MOBILE TELEPHONY IN THE REPUBLIC OF MACEDONIA

, 2016
Slavica Nasteska
openalex   +2 more sources

Research and implementation of the key technology of digital lock-in amplifier [PDF]

, 2017
Wei Liu, Jia You, Cheng Feng, Xisheng Li
openalex   +1 more source

Wmid-f54bd2 - Key (locking)

, 2022
PotteriesMuseum
openalex   +1 more source

Device Integration Technology for Practical Flexible Electronics Systems

Advanced Functional Materials, EarlyView.
Flexible device integration technologies are essential for realizing practical flexible electronic systems. In this review paper, wiring and bonding techniques critical for the industrial‐scale manufacturing of wearable devices are emphasized based on flexible electronics.
Masahito Takakuwa, Kento Yamagishi, Sunghoon Lee, Yusaku Tagawa, Takao Someya, Tomoyuki Yokota   +5 more
wiley   +1 more source

Boosting the photocatalytic H₂O₂ production of covalent organic frameworks with a heteroatom-locked acceptor and gas diffusion system. [PDF]

Chem Sci
Nan Q, Ning J, Han B, Wei H, Wang X, Gu YY, Zhou S, Cao G, Zhang G, Li X, Jia Y, Hao L.   +11 more
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

Design of a Novel, Modular, Mouth Retractor: A Concept Testing Study. [PDF]

Acta Med Philipp
Aljibe MSO, Apacible AMC, Fullante PB.
europepmc   +1 more source

A formal approach to the molecular fuzzy lock-and-key [PDF]

, 2013
Rodrick Wallace
openalex   +1 more source