A systematic review of the application of computational grounded theory method in healthcare research. [PDF]
Biol Methods ProtocShankar R, Devi F, Qian X.
europepmc +1 more source
Reevaluating the Activity of ZIF‐8 Based FeNCs for Electrochemical Ammonia Production
Advanced Functional Materials, EarlyView.Though receiving much attention, the field of electrochemical nitrogen reduction reaction (eNRR) to ammonia is marked by doubts about whether this reaction is possible in aqueous media. This work sheds light on this question for iron single‐atom on N‐doped carbon (FeNC) catalysts—a class of well‐known catalysts that is also worth testing for the sister
Caroline Schneider +6 more
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A hybrid framework of hesitant fuzzy soft sets and rough sets for uncertainty modelling. [PDF]
Sci RepJahanvi, Nishad DK, Singh R, Khalid S.
europepmc +1 more source
Physically-relativized Church–Turing Hypotheses: Physical foundations of computing and complexity theory of computational physics [PDF]
, 2009 Martin Ziegler
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Meta-Mathematics of Computational Complexity Theory
We survey results on the formalization and independence of mathematical statements related to major open problems in computational complexity theory. Our primary focus is on recent findings concerning the (un)provability of complexity bounds within theories of bounded arithmetic.
openaire +2 more sources
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
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Density-functional tight binding meets Maxwell: unraveling the mysteries of (strong) light-matter coupling efficiently. [PDF]
NanophotonicsSidler D +5 more
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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 +7 more
wiley +1 more source