Results 231 to 240 of about 23,925,489 (332)

Research on equivalent simplified modeling and simulation of digital hydraulic cylinder. [PDF]

Sci Rep
Ma C, Hao L, Gao Y.
europepmc   +1 more source

Kentucky Warbler (Vol. 79, no. 2) [PDF]

, 2003
Kentucky Library Research Collections,
core   +4 more sources

Band Alignment in In‐Oxo Metal Porphyrin SURMOF Heterojunctions

Advanced Functional Materials, EarlyView.
Porphyrin core metalation in indium‑oxo SURMOFs enables systematic tuning of band edge positions without altering the crystal structure. First‑principles calculations reveal type‑I and type‑II heterostructures as well as multi‑junction energy cascades, establishing a modular strategy for exciton funneling and charge separation in optoelectronic ...
Puja Singhvi, Nina Vankova, Thomas Heine
wiley   +1 more source

Ex Vivo Assessment of Keratinocytic Carcinomas Using a Fast Mid-Infrared Probe. [PDF]

J Biophotonics
Vazquez de Vasquez MG, Bradley RC, Hitchcock CL, Casey AS, Cusick A, Coe JV, Siegle R.   +6 more
europepmc   +1 more source

Kentucky Warbler (Vol. 29, no. 3) [PDF]

, 1953
Kentucky Library Research Collections,
core   +4 more sources

Microporous Microgel Assemblies Facilitating the Recruitment and Osteogenic Differentiation of Progenitor Cells for Bone Regeneration

Advanced Functional Materials, EarlyView.
There is a significant need for biomaterials with well‐defined stability and bioactivity to support tissue regeneration. In this study, we developed a tunable microgel platform that enables the decoupling of stiffness from porosity, thereby promoting bone regeneration.
Silvia Pravato, Haishuo Guan, Bianca M. Carrara, Mira Jacobs, Matteo Monticelli, Lukas Moser, Thomas Moragues, Andrew J. deMello, Tomaso Zambelli, Martin Ehrbar   +9 more
wiley   +1 more source

A high diversity naïve variable new antigen receptor, vNAR, phage library for rapid nanobody discovery across diverse antigens. [PDF]

J Biol Chem
Kumar V, Jangid K, Santhosh B, Dixit R, Yadav U, Verma S, Rout A, Surya S, Das M, Gupta R, Saroj A, Madhukalya R, Gupta M, Kalra M, Iqbal H, Kumar D, Sinha S, Tomar S, Kumar P, Kumar R.   +19 more
europepmc   +1 more source

Kentucky Warbler (Vol. 22, no. 4) [PDF]

, 1946
Kentucky Library Research Collections,
core   +4 more sources

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

Screening for Peptides to Bind and Functionally Inhibit SARS-CoV-2 Fusion Peptide Using Mirrored Combinatorial Phage Display and Human Proteomic Phage Display. [PDF]

Molecules
Pal A, Roy NS, Angeliadis M, Madhu P, O'Reilly S, Bera I, Francois N, Lynch A, Gautier V, Devocelle M, O'Connell DJ, Shields DC.   +11 more
europepmc   +1 more source