Results 211 to 220 of about 1,667,560 (344)

Generalizing Minimal Dark Matter: Millicharge or Decay [PDF]

, 2017
Eugenio Del Nobile
openalex   +1 more source

Decoupling Size and Electronic Effects in Doped SrTiO3 Photocatalysts Through Surface Area–Normalized CO2 Hydrogenation Rates

Advanced Functional Materials, EarlyView.
Exploring the photocatalytic reverse water–gas shift (RWGS) reaction on doped SrTiO3 nanoparticle films, reveals that normalizing catalytic rates by the catalyst's specific surface area (SSA) disentangled surface area effects from the catalyst's intrinsic material properties.
Dikshita Bhattacharyya, Baliana Shani, Isaac Holmes‐Gentle, Gerardo T Martinez, Martyn McLachlan, Nicola Seriani, Ludmilla Steier   +6 more
wiley   +1 more source

Cleaning a dark matter detector: A case of ontological and normative elusiveness. [PDF]

Soc Stud Sci
de Swart J, Mol A.
europepmc   +1 more source

Tidal Tales Two: The Effect of Dark Matter Halos on Tidal Tail Morphology and Kinematics [PDF]

, 1997
J. Christopher Mihos, John Dubinski, Lars Hernquist   +2 more
openalex   +1 more source

Dark Matters

Scientific American, 2010
openaire   +2 more sources

Photoswitching Conduction in Framework Materials

Advanced Functional Materials, EarlyView.
This mini‐review summarizes recent advances in state‐of‐the‐art proton and electron conduction in framework materials that can be remotely and reversibly switched on and off by light. It discusses the various photoswitching conduction mechanisms and the strategies employed to enhance photoswitched conductivity.
Helmy Pacheco Hernandez, Stefan Hecht, Wolfgang Wenzel, Lars Heinke, Mariana Kozlowska   +4 more
wiley   +1 more source

Overview of the ESCAPE Dark Matter Test Science Project for astronomers. [PDF]

Open Res Eur
Pearson J, Dickinson H, Sinha S, Serjeant S.   +3 more
europepmc   +1 more source

Black hole echoes in dark matter halo [PDF]

, 2021
Dong Liu, Yi Yang, Shu-Rui Wu, Yujia Xing, Zhaoyi Xu, Zheng‐Wen Long   +5 more
openalex  

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

Global Distribution Patterns of Dark Matter Fungi in Cold Seep: A Metagenomic Meta-Analysis. [PDF]

J Fungi (Basel)
Zhang ZF, Jiang Y, Mao J.
europepmc   +1 more source