Results 101 to 110 of about 1,566,699 (318)
Advanced Functional Materials, EarlyView.The study presents biodegradable and recyclable mixed‐matrix membranes (MMMs), hydrogels, and cryogels using luminescent nanoscale metal‐organic frameworks (nMOFs) and biopolymers. These bio‐nMOF‐MMMs combine europium‐based nMOFs as probes for the status of the materials with the biopolymers agar and gelatine and present alternatives to conventional ...
Moritz Maxeiner +4 more
wiley +1 more source
On the non-local heat kernel expansion
, 2013 We propose a novel derivation of the non-local heat kernel expansion, first studied by Barvinsky, Vilkovisky and Avramidi, based on simple diagrammatic equations satisfied by the heat kernel. For Laplace-type differential operators we obtain the explicit
Alessandro Codello +2 more
core +1 more source
Understanding Decoherence of the Boron Vacancy Center in Hexagonal Boron Nitride
Advanced Functional Materials, EarlyView.State‐of‐the‐art computations unravel the intricate decoherence dynamics of the boron vacancy center in hexagonal boron nitride across magnetic fields from 0 to 3 T. Five distinct regimes emerge, dominated by nuclear spin interactions, revealing optimal coherence times of 1–20 µs in the 180–350 mT range for isotopically pure samples.
András Tárkányi, Viktor Ivády
wiley +1 more source
Mining Point Cloud Local Structures by Kernel Correlation and Graph Pooling
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2017 Unlike on images, semantic learning on 3D point clouds using a deep network is challenging due to the naturally unordered data structure. Among existing works, PointNet has achieved promising results by directly learning on point sets.
Yiru Shen +3 more
semanticscholar +1 more source
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
Phenology and pomology of almond’s cultivars and genotypes using multivariate analysis
Advances in Horticultural Science, 2017 The present research aimed to study the flower and fruit properties of 60 almond’s cultivars and genotypes. All fruit and kernel traits had high heritability (ranged from 20.73 to 92.13%). Double kernel and pistil length showed the most and few genotypic
Ali Imani, Mansoore Shamili
doaj +1 more source
Crack‐Growing Interlayer Design for Deep Crack Propagation and Ultrahigh Sensitivity Strain Sensing
Advanced Functional Materials, EarlyView.A crack‐growing semi‐cured polyimide interlayer enabling deep cracks for ultrahigh sensitivity in low‐strain regimes is presented. The sensor achieves a gauge factor of 100 000 at 2% strain and detects subtle deformations such as nasal breathing, highlighting potential for minimally obstructive biomedical and micromechanical sensing applications ...
Minho Kim +11 more
wiley +1 more source
Noncommutative Heat Kernel [PDF]
Letters in Mathematical Physics, 2004 We consider a natural generalisation of the Laplace type operators for the case of non-commutative (Moyal star) product. We demonstrate existence of a power law asymptotic expansion for the heat kernel of such operators on T^n. First four coefficients of this expansion are calculated explicitly.
openaire +2 more sources
Local Thermal Conductivity Patterning in Rotating Lattice Crystals of Anisotropic Sb2S3
Advanced Functional Materials, EarlyView.Microscale control of thermal conductivity in Sb2S3 is demonstrated via laser‐induced rotating lattice crystals. Thermal conductivity imaging reveals marked thermal transport anisotropy, with the c axis featuring amorphous‐like transport, whereas in‐plane directions (a, b) exhibit 3.5x and 1.7x larger thermal conductivity.
Eleonora Isotta +13 more
wiley +1 more source
Kernel density estimation and its application
, 2018 Kernel density estimation is a technique for estimation of probability density function that is a must-have enabling the user to better analyse the studied probability distribution than when using a traditional histogram. Unlike the histogram, the kernel
S. Wȩglarczyk
semanticscholar +1 more source