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Phase Engineering of Nanomaterials (PEN): Evolution, Current Challenges, and Future Opportunities
This review summarizes the synthesis, phase transition, advanced characterization spanning ex situ to in situ and operando techniques, and diverse applications of phase engineering of nanomaterials (PEN). It further outlines key challenges and future opportunities, such as phase stability, architecture control, and artificial intelligence (AI)‐driven ...
Ye Chen +7 more
wiley +1 more source
Field‐programmed anisotropy imparts material‐level intelligence to soft composites. Magnetic torque dynamics drive magneto‐piezoelectric nanowires from random dispersion into deterministic axial alignment. The resulting hierarchical percolation networks confine electromechanical coupling to specific axes and intrinsically decouple superposed force ...
Yubin Kim +9 more
wiley +1 more source
Electrically Tunable Heliconical Smectic Superstructure in Polar Fluids
Strong dipole–dipole interactions give rise to an emergent polar smectic phase with spontaneous chiral symmetry breaking. This resulting polar heliconical smectic superstructure enables novel functionalities in polar fluids, such as color modulation driven by electric field strength and frequency, as well as second‐harmonic generation amplification ...
Hiroya Nishikawa +5 more
wiley +1 more source
Field‐Programmable Mobile Magneto‐Photonic Metaparticles for Active Light Manipulation and Steering
We report magneto‐photonic metaparticles that integrate magnetic actuation with nanoimprinted metagratings, enabling mobile and programmable beam steering in aqueous environments. This platform achieves real time, directional light control beyond static or line‐of‐sight systems, while remaining compatible with scalable fabrication, providing a ...
Seung Yeol Lee, Yujie Luo, Ognjen Ilic
wiley +1 more source
Selective methane (CH4) photooxidation emerges from architectural decoupling of activation and preservation. Programmable organic semiconductor frameworks spatially organize oxidative flux and intermediate residence, separating reaction zones from product domains to synchronize activation strength with stabilization and establish a selective oxidation ...
Minxian Zhang +5 more
wiley +1 more source
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The generalized maximum entropy principle
IEEE Transactions on Systems, Man, and Cybernetics, 1989Generalizations of the maximum entropy principle (MEP) of E.T. Jaynes (1957) and the minimum discrimination information principle (MDIP) of S. Kullback (1959) are described. The generalizations have been achieved by enunciating the entropy maximization postulate and examining its consequences.
H K Kesavan, J N Kapur
exaly +2 more sources
The generalised maximum α entropy principle
International Journal of Mathematics in Operational Research, 2018Generalisations of maximum entropy principle (MEP) and minimum discrimination information principle (MDIP) are described by Kapur and Kesavan (1989) . In this paper, we used generalised entropies and replaced Shannon entropy with Tsallis entropy when α = 2 .
G R Mohtashami Borzadaran
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Entropy estimation using the principle of maximum entropy
2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011In this paper, we present a novel entropy estimator for a given set of samples drawn from an unknown probability density function (PDF). Counter to other entropy estimators, the estimator presented here is parametric. The proposed estimator uses the maximum entropy principle to offer anm-term approximation to the underlying distribution and does not ...
Behrouz Behmardi +2 more
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Probability distributions and the maximum entropy principle
Applied Mathematics and Computation, 2023zbMATH Open Web Interface contents unavailable due to conflicting licenses.
José Villa-Morales, Luis Rincón
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The principle of maximum entropy
The Mathematical Intelligencer, 1985The authors point out that the ''principle of maximum entropy'' can be considered as a variational principle which has applications in statistical mechanics, in decision theory, in pattern-recognition and in time-series analysis. They explain this principle as follows: From the set of all probability distributions (for instance, the possible ...
Guiasu, Silviu, Shenitzer, Abe
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