Results 281 to 290 of about 2,817,119 (330)

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

Accuracy and reliability of 3D cephalometric landmark detection with deep learning. [PDF]

Eur J Med Res
Liu B, Liu C, Xiong Y, Zhu H, Zeng W, Chen J, Guo J, Liu W, Tang W.   +8 more
europepmc   +1 more source

Electron Compensation Enhanced Triboelectric Sensor Assisted by Machine Learning for Tactile Perception Recognition

Advanced Functional Materials, EarlyView.
Integrating polyethyleneimine and carbon black into polyurethane enhances electron transport and mechanical durability. The resulting sensor achieves significantly improved electrical signal and sensitivity, enabling efficient machine learning‐based tactile signal recognition in bionic applications.
Xiangkun Bo, Wei Wang, Hong Zhao, Ruiqin Zhang, Walid A. Daoud   +4 more
wiley   +1 more source

Using In Situ TEM to Understand the Surfaces of Electrocatalysts at Reaction Conditions: Single‐Atoms to Nanoparticles

Advanced Functional Materials, EarlyView.
This review summarizes recent advances in closed‐cell in situ TEM strategies for accurate determination of the activity and stability of single‐atom catalyst systems during operation. Operando conditions causing dynamic changes of SAC systems are highlighted and we explain why ensemble average‐based optical techniques may benefit from the technological
Martin Ek, Soshan Cheong, Julie Cairney, Richard D. Tilley, Ingemar Persson   +4 more
wiley   +1 more source

Terrier: a deep learning repeat classifier. [PDF]

Brief Bioinform
Turnbull R, Young ND, Tescari E, Skerratt LF, Kosch TA.   +4 more
europepmc   +1 more source

Advanced Cellulose‐Based Gels for Wearable Physiological Monitoring: From Fiber Modification to Application Optimization

Advanced Functional Materials, EarlyView.
This review discusses cellulose‐based hydrogels technology, analyzes their application progress in physiological signal monitoring, and explores the effects of pretreatment, crosslinking, and molding methods on gel performance, to provide valuable insights into the efficient utilization of plant fibers and the environmentally friendly development of ...
Zhiming Wang, Jinxuan Jiang, Kexin Wei, Xiaojian Zhou, Muhammad Wakil Shahzad, Yifan Li, Xuehua Zhang, Ben Bin Xu, Shengbo Ge   +8 more
wiley   +1 more source

A refined lion optimizer for deep learning. [PDF]

Sci Rep
Rong J, Ma C, Zhang Q, Cao Y, Kou W.
europepmc   +1 more source

Ice Lithography: Recent Progress Opens a New Frontier of Opportunities

Advanced Functional Materials, EarlyView.
This review focuses on recent advancements in ice lithography, including breakthroughs in compatible precursors and substrates, processes and applications, hardware, and digital methods. Moreover, it offers a roadmap to uncover innovation opportunities for ice lithography in fields such as biological, nanoengineering and microsystems, biophysics and ...
Bingdong Chang, Gwendoline A. E. Anand, Rubaiyet Haque, Dylan A. Chiaro, Mona Sharma, Andrew R. Williams, Frank Niklaus, Daryl W. Yee, Gavin M. King, Anpan Han   +9 more
wiley   +1 more source

A Review on MXene Terminations

Advanced Functional Materials, EarlyView.
Surface terminations play a decisive role in determining the properties of MXenes. Their coordination, composition and stoichiometry can be tuned both during synthesis and through post synthesis methods to tailor MXenes for optimum performance in applications. This review explores the present understanding of surface terminations.
Hari Hara Sudhan Thangavelu, Changjie Huang, Florian Chabanais, Justinas Palisaitis, Per O. Å. Persson   +4 more
wiley   +1 more source