Results 131 to 140 of about 420 (241)
THE EFFECT OF HYPOTONIC AND HYPERTONIC SOLUTIONS ON FIBROBLASTS OF THE EMBRYONIC CHICK HEART IN VITRO. [PDF]
Hogue MJ.
europepmc +1 more source
The study designed the nanocellulose‐induced “surface‐locking” strategy to stabilize the interface between MnO2 and a carbon‐based substrate. The dissolution of MnO2 was prominently curbed by imparting C–O–Mn bonding and optimized wettability. Zn–MnO2 batteries were endowed with outstanding cyclic stability and improved capacity.
Meng Zhang +10 more
wiley +1 more source
This study proposed a structure‐designed benzohydrazide derivative, namely 4‐methoxybenzohydrazide (MeOBH), as an additive to regulate crystallization, passivate defects, optimize energy‐level structure, and enhance phase stability of β‐CsPbI3 perovskite films.
Haiyan Zhao +10 more
wiley +1 more source
Searching for the Source of the Leak: PIE and the Macklin Effect [PDF]
Derek W. Russell +2 more
openaire +2 more sources
Connecticut College Alumnae News, December 1965 [PDF]
Connecticut College
core +4 more sources
ABSTRACT The inherent hydroxide‐rich (OH⁻) environment in alkaline media facilitates the two‐electron oxygen reduction reaction (2e−ORR). However, the strong interaction between alkali metal cations and solvated water molecules significantly reduces the connectivity of the hydrogen bond network within the alkaline electric double layer, thereby ...
Kaiming Li +11 more
wiley +1 more source
An ultrastrong, hierarchically nanoporous gel polymer electrolyte (GPE) was fabricated via poly(ionic liquid)‐induced interfacial coacervation of cellulose nanofibrils. Its cascade ion‐conduction network enables dual‐mode Li⁺ transport via nanoconfinement and interstitial hopping. The GPE enables stable cycling of high mass loading battery (LiFePO₄, 16
Dong Lv +7 more
wiley +1 more source
Data‐Driven Design of Scalable Perovskite Film Fabrication via Machine Learning–Guided Processing
Considering complex process parameters and poor reproducibility in perovskite thin film fabrication, this study uses machine learning to analyze and predict high‐dimensional process variables. The Random Forest model, identified as the most effective, can effectively analyze and rapidly predict optimal process parameters from extensive data.
Hong Liu +9 more
wiley +1 more source

