Results 141 to 150 of about 29,700 (267)

Understanding Functional Materials at School

Advanced Functional Materials, EarlyView.
This review outlines strategies for effectively teaching nanoscience in schools, focusing on challenges such as scale comprehension and curriculum integration. Emphasizing inquiry‐based learning and chemistry core concepts, it showcases hands‐on activities, digital tools, and interdisciplinary approaches.
Johannes Claußnitzer, Jürgen Paul
wiley   +1 more source

BTFormer: Blast transformer for human blastocyst components segmentation. [PDF]

PLoS One
Wang H, Li Y, Qiu L, Zhang J, Hu J.
europepmc   +1 more source

Magnetic‐Field Tuning of the Spin Dynamics in the Quasi‐2D Van der Waals Antiferromagnet CuCrP2S6

Advanced Functional Materials, EarlyView.
This study reveals 2D character of the spin dynamics in CuCrP2S6, as well as complex field dependence of collective excitations in the antiferromagnetically ordered state. Their remarkable tuning from the antiferromagnetic to the ferromagnetic type with magnetic field, together with the non‐degeneracy of the magnon gaps favorable for the induction of ...
Joyal John Abraham, Yaqian Guo, Yuliia Shemerliuk, Sebastian Selter, Saicharan Aswartham, Kranthi Kumar Bestha, Laura T. Corredor, Anja U. B. Wolter, Olga Kataeva, Luka Rogić, Noah Somun, Damjan Pelc, Oleg Janson, Jeroen van den Brink, Bernd Büchner, Vladislav Kataev, Alexey Alfonsov   +16 more
wiley   +1 more source

A parameter centric service discovery framework for social digital twins in smart City. [PDF]

Sci Rep
Amin F, Khan S, Choi GS, Abid MA, de la Torre I, Montero EC, Noya ID.   +6 more
europepmc   +1 more source

Steep‐Switching Memory FET for Noise‐Resistant Reservoir Computing System

Advanced Functional Materials, EarlyView.
We demonstrate the steep‐switching memory FET with CuInP2S6/h‐BN/α‐In2Se3 heterostructure for application in noise‐resistant reservoir computing systems. The proposed device achieves steep switching characteristics (SSPGM = 19 mV/dec and SSERS = 23 mV/dec) through stabilization between CuInP2S6 and h‐BN.
Seongkweon Kang, Jaerok Kim, Sang‐Min Lee, Sungpyo Baek, Saeroonter Oh, Yoonmyung Lee, Sungjoo Lee   +6 more
wiley   +1 more source

PepLand: a large-scale pre-trained peptide representation model for a comprehensive landscape of both canonical and non-canonical amino acids. [PDF]

Brief Bioinform
Zhang R, Wu H, Liu C, Yang Q, Xiu Y, Li K, Chen N, Wang Y, Wang Y, Gao X, Zhou F.   +10 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

Exploring the intersection of natural sciences and information technology via entropy and randomness. [PDF]

Nat Commun
Luescher AM, Heckel R, Grass RN.
europepmc   +1 more source

Ce3+‐Activated Lithium Rare‐Earth Oxonitridolithosilicates: A New Class of LED Phosphors with Similarities to Garnets

Advanced Functional Materials, EarlyView.
Garnet‐like luminescence upon Ce3+‐activation is found in the first six representatives Li9RESi4N4O8 (RE = Y, La, Gd, Dy, Yb, Lu) of the substance class of lithium rare‐earth oxonitridolithosilicates, which also show a novel structure type. High‐temperature solid‐state synthesis yielded crystalline samples for single‐crystal X‐ray diffraction‐based ...
Kilian M. Rießbeck, Markus Seibald, Christiane Stoll, Anja Köbler, Markus Suta, Hubert Huppertz   +5 more
wiley   +1 more source