Results 121 to 130 of about 52,742 (268)
Evaluation of Convolution Sums entailing mixed Divisor Functions for a Class of Levels
, 2021 Ebénézer Ntienjem
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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 +9 more
wiley +1 more source
Advanced Functional Materials, EarlyView.This review explores functional and responsive materials for triboelectric nanogenerators (TENGs) in sustainable smart agriculture. It examines how particulate contamination and dirt affect charge transfer and efficiency. Environmental challenges and strategies to enhance durability and responsiveness are outlined, including active functional layers ...
Rafael R. A. Silva +9 more
wiley +1 more source
Evaluation of the Convolution Sum involving the Sum of Divisors Function\n for 14, 22 and 26 [PDF]
, 2016 Ayşe Alaca +2 more
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Defects Dynamic in Photo‐Excited CeO2 and their Influence on CO2 Photoreduction
Advanced Functional Materials, EarlyView.X‐ray photoelectron spectroscopy study under light excitation is presented to track the defect dynamic (Ce4+ to Ce3+) in CeO2. Surface enhanced Raman spectroscopy confirmed the key role of Ce3+ states in controlling charge and energy transfer across the CeO2‐dye molecule interface.
Rambabu Yalavarthi +3 more
wiley +1 more source
The convolution sum $\sum_{al+bm=n} \sigma(l) \sigma(m)$ for $(a,b)=(1,28), (4,7), (1,14), (2,7), (1,7)$ [PDF]
, 2016 Ayşe Alaca +2 more
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Convolution Sums Related to Fibonacci Numbers and Lucas Numbers
, 2016 Aeran Kim
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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
Uniform approximations by Fourier sums on the sets of convolutions of periodic functions of high smoothness [PDF]
, 2023 A. S. Serdyuk +1 more
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