Fundamental limits of thermal radiation are imposed by Kirchhoff's law, which assumes the electromagnetic reciprocity of a material or material system. Thus, breaking reciprocity can enable breaking barriers in thermal efficiency engineering. In this work, we present a subwavelength, 1D photonic crystal composed of Weyl semimetal and dielectric layers, whose structure was optimized to maximize the nonreciprocity of infrared radiation absorptance in a planar and compact design. To engineer an ultra-compact absorber structure that does not require gratings or prisms to couple light, we used a genetic algorithm (GA) to maximize nonreciprocity in the design globally, followed by the application of the numerical gradient ascent (GAGA) algorithm as a local optimization to further enhance the design. We chose Weyl semimetals as active layers in our design as they possess strong, intrinsic nonreciprocity, and do not require an external magnetic field. The resulting GAGA-generated 1D magnetophotonic crystal offers high nonreciprocity (quantified by absorptance contrast) while maintaining an ultra-compact design with much fewer layers than prior work. We account for both s- and p-polarized absorptance spectra to create a final, eight-layer design suitable for thermal applications, which simultaneously minimizes the parasitic, reciprocal absorptance of s-polarized light.
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http://dx.doi.org/10.1515/nanoph-2023-0598 | DOI Listing |
PLOS Digit Health
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Centre Référent Maladies Rares Neuromusculaires, Service de Médecine Physique et de Réadaptation Pédiatrique des Hospices Civils de Lyon - Hôpital Femme Mère Enfant, Bron, France.
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Department of Respiratory and Critical Care Medicine, Medical School of Nantong University, Nantong Key Laboratory of Respiratory Medicine, Affiliated Hospital of Nantong University, Nantong, 226001, China.
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View Article and Find Full Text PDFDiscov Oncol
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Department of Medical Imaging, Shenzhen Longhua District Key Laboratory of Neuroimaging, Shenzhen Longhua District Central Hospital, Shenzhen, 518110, China.
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J Gen Virol
January 2025
Department of Infection Biology, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, UK.
The complexity and speed of evolution in viruses with RNA genomes makes predictive identification of variants with epidemic or pandemic potential challenging. In recent years, machine learning has become an increasingly capable technology for addressing this challenge, as advances in methods and computational power have dramatically improved the performance of models and led to their widespread adoption across industries and disciplines. Nascent applications of machine learning technology to virus research have now expanded, providing new tools for handling large-scale datasets and leading to a reshaping of existing workflows for phenotype prediction, phylogenetic analysis, drug discovery and more.
View Article and Find Full Text PDFElife
January 2025
Allen Discovery Center, Tufts University, Medford, United States.
Many applications in biomedicine and synthetic bioengineering rely on understanding, mapping, predicting, and controlling the complex behavior of chemical and genetic networks. The emerging field of diverse intelligence investigates the problem-solving capacities of unconventional agents. However, few quantitative tools exist for exploring the competencies of non-conventional systems.
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