Deep learning based analysis of microstructured materials for thermal radiation control | Scientific Reports – Nature.com
Raman, A. P., Anoma, M. A., Zhu, L., Rephaeli, E. & Fan, S. Passive radiative cooling below ambient air temperature under direct sunlight. Nature 515, 540544 (2014).
ADS CAS PubMed Article Google Scholar
Zhai, Y. et al. Scalable-manufactured randomized glass-polymer hybrid metamaterial for daytime radiative cooling. Science 355, 10621066 (2017).
ADS CAS PubMed Article Google Scholar
Li, P. et al. Large-scale nanophotonic solar selective absorbers for high-efficiency solar thermal energy conversion. Adv. Mater. 27, 45854591 (2015).
CAS PubMed Article Google Scholar
Kumar, R. & Rosen, M. A. Thermal performance of integrated collector storage solar water heater with corrugated absorber surface. Appl. Therm. Eng. 30, 17641768 (2010).
Article Google Scholar
Planck, M. The Theory of Heat Radiation (P. Blakinstons Son & Co., 1914).
MATH Google Scholar
Zhu, J., Hsu, C. M., Yu, Z., Fan, S. & Cui, Y. Nanodome solar cells with efficient light management and self-cleaning. Nano Lett. 10, 19791984 (2010).
ADS CAS PubMed Article Google Scholar
Zhou, L., Yu, X. & Zhu, J. Metal-core/semiconductor-shell nanocones for broadband solar absorption enhancement. Nano Lett. 14, 10931098 (2014).
ADS CAS PubMed Article Google Scholar
Lee, B. J., Chen, Y. B., Han, S., Chiu, F. C. & Lee, H. J. Wavelength-selective solar thermal absorber with two-dimensional nickel gratings. J. Heat Transfer 136, 17 (2014).
Google Scholar
Yin, X., Yang, R., Tan, G. & Fan, S. Terrestrial radiative cooling: Using the cold universe as a renewable and sustainable energy source. Science 370, 786791 (2020).
ADS CAS PubMed Article Google Scholar
Nie, X. et al. Cool white polymer coatings based on glass bubbles for buildings. Sci. Rep. 10, 110 (2020).
ADS Article CAS Google Scholar
Mandal, J. et al. Hierarchically porous polymer coatings for highly efficient passive daytime radiative cooling. Science 362, 315319 (2018).
ADS CAS PubMed Article Google Scholar
Zhang, H. et al. Biologically inspired flexible photonic films for efficient passive radiative cooling. Proc. Natl. Acad. Sci. 117, 202001802 (2020).
Google Scholar
Krishna, A. et al. Ultraviolet to mid-infrared emissivity control by mechanically reconfigurable graphene. Nano Lett. 19, 50865092 (2019).
ADS CAS PubMed Article Google Scholar
Sala-Casanovas, M., Krishna, A., Yu, Z. & Lee, J. Bio-inspired stretchable selective emitters based on corrugated nickel for personal thermal management. Nanoscale Microscale Thermophys. Eng. 23, 173187 (2019).
ADS CAS Article Google Scholar
Sullivan, J., Yu, Z. & Lee, J. Optical analysis and optimization of micropyramid texture for thermal radiation control. Nanoscale Microscale Thermophys. Eng. https://doi.org/10.1080/15567265.2021.1958960 (2021).
Article Google Scholar
Campbell, P. & Green, M. A. Light trapping properties of pyramidally textured surfaces. J. Appl. Phys. 62, 243249 (1987).
ADS Article Google Scholar
Leon, J. J. D., Hiszpanski, A. M., Bond, T. C. & Kuntz, J. D. Design rules for tailoring antireflection properties of hierarchical optical structures. Adv. Opt. Mater. 5, 18 (2017).
Google Scholar
Zhang, T. et al. Black silicon with self-cleaning surface prepared by wetting processes. Nanoscale Res. Lett. 8, 15 (2013).
ADS CAS Article Google Scholar
Liu, Y. et al. Hierarchical robust textured structures for large scale self-cleaning black silicon solar cells. Nano Energy 3, 127133 (2014).
CAS Article Google Scholar
Dimitrov, D. Z. & Du, C. H. Crystalline silicon solar cells with micro/nano texture. Appl. Surf. Sci. 266, 14 (2013).
ADS CAS Article Google Scholar
Peter Amalathas, A. & Alkaisi, M. M. Efficient light trapping nanopyramid structures for solar cells patterned using UV nanoimprint lithography. Mater. Sci. Semicond. Process. 57, 5458 (2017).
Article CAS Google Scholar
Mavrokefalos, A., Han, S. E., Yerci, S., Branham, M. S. & Chen, G. Efficient light trapping in inverted nanopyramid thin crystalline silicon membranes for solar cell applications. Nano Lett. 12, 27922796 (2012).
ADS CAS PubMed Article Google Scholar
Rahman, T., Navarro-Ca, M. & Fobelets, K. High density micro-pyramids with silicon nanowire array for photovoltaic applications. Nanotechnology 25, 485202 (2014).
PubMed Article CAS Google Scholar
Singh, P. et al. Fabrication of vertical silicon nanowire arrays on three-dimensional micro-pyramid-based silicon substrate. J. Mater. Sci. 50, 66316641 (2015).
ADS CAS Article Google Scholar
Zhu, J. et al. Optical absorption enhancement in amorphous silicon nanowire and nanocone arrays. Nano Lett. 9, 279282 (2009).
ADS PubMed Article CAS Google Scholar
Wei, W. R. et al. Above-11%-efficiency organic-inorganic hybrid solar cells with omnidirectional harvesting characteristics by employing hierarchical photon-trapping structures. Nano Lett. 13, 36583663 (2013).
ADS CAS PubMed Article Google Scholar
Peng, Y. J., Huang, H. X. & Xie, H. Rapid fabrication of antireflective pyramid structure on polystyrene film used as protective layer of solar cell. Sol. Energy Mater. Sol. Cells 171, 98105 (2017).
CAS Article Google Scholar
Sai, H., Yugami, H., Kanamori, Y. & Hane, K. Solar selective absorbers based on two-dimensional W surface gratings with submicron periods for high-temperature photothermal conversion. Sol. Energy Mater. Sol. Cells 79, 3549 (2003).
CAS Article Google Scholar
Deinega, A., Valuev, I., Potapkin, B. & Lozovik, Y. Minimizing light reflection from dielectric textured surfaces. J. Opt. Soc. Am. A 28, 770 (2011).
ADS Article Google Scholar
Shore, K. A. Numerical methods in photonics, by Andrei V. Lavrinenko, Jesper Laegsgaard, Niles Gregersen, Frank Schmidt, and Thomas Sondergaard. Contemporary Physics vol. 57 (2016).
Malkiel, I. et al. Plasmonic nanostructure design and characterization via deep learning. Light Sci. Appl. 7, 18 (2018).
CAS Article Google Scholar
Bojarski, M. et al. End to End Learning for Self-Driving Cars. 19 (2016).
Hinton, G. et al. Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal Process. Mag. 29, 1617 (2012).
Article Google Scholar
Spantideas, S. T., Giannopoulos, A. E., Kapsalis, N. C. & Capsalis, C. N. A deep learning method for modeling the magnetic signature of spacecraft equipment using multiple magnetic dipoles. IEEE Magn. Lett. 12, 15 (2021).
Article Google Scholar
Xiong, Y., Guo, L., Tian, D., Zhang, Y. & Liu, C. Intelligent optimization strategy based on statistical machine learning for spacecraft thermal design. IEEE Access 8, 204268204282 (2020).
Article Google Scholar
Zhang, C. A Statistical Machine Learning Based Modeling and Exploration Framework for Run-Time Cross-Stack Energy Optimization (University of North Carolina at Charlotte, 2013).
Book Google Scholar
Zhu, W. et al. Optimization of the thermophysical properties of the thermal barrier coating materials based on GA-SVR machine learning method: Illustrated with ZrO2doped DyTaO4system. Mater. Res. Express 8, 125503 (2021).
ADS CAS Article Google Scholar
Zhang, T. et al. Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency. Opt. Express 28, 18899 (2020).
ADS CAS PubMed Article Google Scholar
Li, X., Shu, J., Gu, W. & Gao, L. Deep neural network for plasmonic sensor modeling. Opt. Mater. Express 9, 3857 (2019).
ADS CAS Article Google Scholar
Baxter, J. et al. Plasmonic colours predicted by deep learning. Sci. Rep. 9, 119 (2019).
ADS Article CAS Google Scholar
He, J., He, C., Zheng, C., Wang, Q. & Ye, J. Plasmonic nanoparticle simulations and inverse design using machine learning. Nanoscale 11, 1744417459 (2019).
CAS PubMed Article Google Scholar
Sajedian, I., Kim, J. & Rho, J. Finding the optical properties of plasmonic structures by image processing using a combination of convolutional neural networks and recurrent neural networks. Microsyst. Nanoeng. 5, 18 (2019).
Article Google Scholar
Han, S., Shin, J. H., Jung, P. H., Lee, H. & Lee, B. J. Broadband solar thermal absorber based on optical metamaterials for high-temperature applications. Adv. Opt. Mater. 4, 12651273 (2016).
CAS Article Google Scholar
Seo, J. et al. Design of a broadband solar thermal absorber using a deep neural network and experimental demonstration of its performance. Sci. Rep. 9, 19 (2019).
ADS Article CAS Google Scholar
Nadell, C. C., Huang, B., Malof, J. M. & Padilla, W. J. Deep learning for accelerated all-dielectric metasurface design. Opt. Express 27, 27523 (2019).
ADS CAS PubMed Article Google Scholar
Deppe, T. & Munday, J. Nighttime photovoltaic cells: Electrical power generation by optically coupling with deep space. ACS Photon. https://doi.org/10.1021/acsphotonics.9b00679 (2019).
Article Google Scholar
Ma, W., Cheng, F. & Liu, Y. Deep-learning-enabled on-demand design of chiral metamaterials. ACS Nano 12, 63266334 (2018).
CAS PubMed Article Google Scholar
Li, Y. et al. Self-learning perfect optical chirality via a deep neural network. Phys. Rev. Lett. 123, 16 (2019).
CAS Google Scholar
Balin, I., Garmider, V., Long, Y. & Abdulhalim, I. Training artificial neural network for optimization of nanostructured VO2-based smart window performance. Opt. Express 27, A1030 (2019).
ADS CAS PubMed Article Google Scholar
Elzouka, M., Yang, C., Albert, A., Prasher, R. S. & Lubner, S. D. Interpretable forward and inverse design of particle spectral emissivity using common machine-learning models. Cell Rep. Phys. Sci. 1, 100259 (2020).
Article Google Scholar
Peurifoy, J. et al. Nanophotonic particle simulation and inverse design using artificial neural networks. arXiv 18 (2017). https://doi.org/10.1117/12.2289195.
An, S. et al. A Deep learning approach for objective-driven all-dielectric metasurface design. ACS Photon. 6, 31963207 (2019).
See the rest here:
Deep learning based analysis of microstructured materials for thermal radiation control | Scientific Reports - Nature.com
- How Humans Could Soon Understand and Talk to Animals, Thanks to Machine Learning - SYFY - November 10th, 2025 [November 10th, 2025]
- Machine learning based analysis of diesel engine performance using FeO nanoadditive in sterculia foetida biodiesel blend - Nature - November 10th, 2025 [November 10th, 2025]
- Machine Learning in Maternal Care - Johns Hopkins Bloomberg School of Public Health - November 10th, 2025 [November 10th, 2025]
- Machine learning-based differentiation of benign and malignant adrenal lesions using 18F-FDG PET/CT: a two-stage classification and SHAP... - November 10th, 2025 [November 10th, 2025]
- How to Better Use AI and Machine Learning in Dermatology, With Renata Block, MMS, PA-C - HCPLive - November 10th, 2025 [November 10th, 2025]
- Avoiding Catastrophe: The Importance of Privacy when Leveraging AI and Machine Learning for Disaster Management - CSIS | Center for Strategic and... - November 10th, 2025 [November 10th, 2025]
- Efferocytosis-related signatures identified via Single-cell analysis and machine learning predict TNBC outcomes and immunotherapy response - Nature - November 10th, 2025 [November 10th, 2025]
- Arc Raiders' use of AI highlights the tension and confusion over where machine learning ends and generative AI begins - PC Gamer - November 3rd, 2025 [November 3rd, 2025]
- From performance to prediction: extracting aging data from the effects of base load aging on washing machines for a machine learning model - Nature - November 3rd, 2025 [November 3rd, 2025]
- Meet 'kvcached': A Machine Learning Library to Enable Virtualized, Elastic KV Cache for LLM Serving on Shared GPUs - MarkTechPost - October 28th, 2025 [October 28th, 2025]
- Bayesian-optimized machine learning boosts actual evapotranspiration prediction in water-stressed agricultural regions of China - Nature - October 28th, 2025 [October 28th, 2025]
- Using machine learning to shed light on how well the triage systems work - News-Medical - October 28th, 2025 [October 28th, 2025]
- Our Last Hope Before The AI Bubble Detonates: Taming LLMs - Machine Learning Week US - October 28th, 2025 [October 28th, 2025]
- Using multiple machine learning algorithms to predict spinal cord injury in patients with cervical spondylosis: a multicenter study - Nature - October 28th, 2025 [October 28th, 2025]
- The diagnostic potential of proteomics and machine learning in Lyme neuroborreliosis - Nature - October 28th, 2025 [October 28th, 2025]
- Using unsupervised machine learning methods to cluster cardio-metabolic profile of the middle-aged and elderly Chinese with general and central... - October 28th, 2025 [October 28th, 2025]
- The prognostic value of POD24 for multiple myeloma: a comprehensive analysis based on traditional statistics and machine learning - BMC Cancer - October 28th, 2025 [October 28th, 2025]
- Reducing inequalities using an unbiased machine learning approach to identify births with the highest risk of preventable neonatal deaths - Population... - October 28th, 2025 [October 28th, 2025]
- Association between SHR and mortality in critically ill patients with CVD: a retrospective analysis and machine learning approach - Diabetology &... - October 28th, 2025 [October 28th, 2025]
- AI-Powered Visual Storytelling: How Machine Learning Transforms Creative Content Production - About Chromebooks - October 28th, 2025 [October 28th, 2025]
- How beauty brand Shiseido nearly tripled revenue per user with machine learning - Performance Marketing World - October 28th, 2025 [October 28th, 2025]
- Magnite introduces machine learning-powered ad podding for streaming platforms - PPC Land - October 26th, 2025 [October 26th, 2025]
- Krafton is an AI first company and will invest 70M USD on machine learning - Female First - October 26th, 2025 [October 26th, 2025]
- Machine learning prediction of bacterial optimal growth temperature from protein domain signatures reveals thermoadaptation mechanisms - BMC Genomics - October 24th, 2025 [October 24th, 2025]
- Data Proportionality and Its Impact on Machine Learning Predictions of Ground Granulated Blast Furnace Slag Concrete Strength | Newswise - Newswise - October 24th, 2025 [October 24th, 2025]
- The Evolution of Machine Learning and Its Applications in Orthopaedics: A Bibliometric Analysis - Cureus - October 24th, 2025 [October 24th, 2025]
- Sentiment Analysis with Machine Learning Achieves 83.48% Accuracy in Predicting Consumer Behavior Trends - Quantum Zeitgeist - October 24th, 2025 [October 24th, 2025]
- Use of machine learning for risk stratification of chest pain patients in the emergency department - BMC Medical Informatics and Decision Making - October 24th, 2025 [October 24th, 2025]
- Mass spectrometry combined with machine learning identifies novel protein signatures as demonstrated with multisystem inflammatory syndrome in... - October 24th, 2025 [October 24th, 2025]
- How Machine Learning Is Shrinking to Fit the Sensor Node - All About Circuits - October 24th, 2025 [October 24th, 2025]
- Machine learning models for mechanical properties prediction of basalt fiber-reinforced concrete incorporating graphical user interface - Nature - October 24th, 2025 [October 24th, 2025]
- Ohio wins national cybersecurity award for fraud solutions using machine learning - Spectrum News NY1 - October 24th, 2025 [October 24th, 2025]
- Itron Partners with Gordian Technologies to Enhance Grid Edge Intelligence with AI and Machine Learning Solutions - Quiver Quantitative - October 24th, 2025 [October 24th, 2025]
- Wearable sensors and machine learning give leg up on better running data - Medical Xpress - October 23rd, 2025 [October 23rd, 2025]
- Geophysical-machine learning tool developed for continuous subsurface geomaterials characterization - Phys.org - October 23rd, 2025 [October 23rd, 2025]
- Ohio wins national cybersecurity award for fraud solutions using machine learning - Spectrum News 1 - October 23rd, 2025 [October 23rd, 2025]
- Machine learning predictions of climate change effects on nearly threatened bird species ( Crithagra xantholaema) habitat in Ethiopia for conservation... - October 23rd, 2025 [October 23rd, 2025]
- A machine learning tool for predicting newly diagnosed osteoporosis in primary healthcare in the Stockholm Region - Nature - October 23rd, 2025 [October 23rd, 2025]
- ECBs New Perspective on Machine Learning in Banking - KPMG - October 23rd, 2025 [October 23rd, 2025]
- Ensemble Machine Learning for Digital Mapping of Soil pH and Electrical Conductivity in the Andean Agroecosystem of Peru - Frontiers - October 21st, 2025 [October 21st, 2025]
- New UA research develops machine learning to address needs of children with autism - AZPM News - October 21st, 2025 [October 21st, 2025]
- NMDSI Speaker Series on Weather Forecasting: What Machine Learning Can and Can't Do, Oct. 23 - Marquette Today - October 21st, 2025 [October 21st, 2025]
- Polyskill Achieves 1.7x Improved Skill Reuse and 9.4% Higher Success Rates through Polymorphic Abstraction in Machine Learning - Quantum Zeitgeist - October 21st, 2025 [October 21st, 2025]
- University of Strathclyde opens admission for MSc in Machine & Deep Learning for Jan 2026 intake - The Indian Express - October 21st, 2025 [October 21st, 2025]
- Reducing Model Biases with Machine Learning Corrections Derived from Ocean Data Assimilation Increments - ESS Open Archive - October 19th, 2025 [October 19th, 2025]
- Unlocking Obesity: Multi-Omics and Machine Learning Insights - Bioengineer.org - October 19th, 2025 [October 19th, 2025]
- Lockheed Martin advances PAC-3 MSE interceptor using artificial intelligence and machine learning - Defence Industry Europe - October 19th, 2025 [October 19th, 2025]
- Semi-automated surveillance of surgical site infections using machine learning and rule-based classification models - Nature - October 19th, 2025 [October 19th, 2025]
- AI and Machine Learning - City of San Jos to release RFP for generative AI platform - Smart Cities World - October 19th, 2025 [October 19th, 2025]
- Machine learning helps identify 'thermal switch' for next-generation nanomaterials - Phys.org - October 17th, 2025 [October 17th, 2025]
- Machine Learning Makes Wildlife Data Analysis Less of a Trek - Maryland.gov - October 17th, 2025 [October 17th, 2025]
- An interpretable multimodal machine learning model for predicting malignancy of thyroid nodules in low-resource scenarios - BMC Endocrine Disorders - October 17th, 2025 [October 17th, 2025]
- In First-Episode Psychosis Patients, Machine Learning Predicted Illness Trajectories to Potentially Improve Outcomes - Brain and Behavior Research - October 17th, 2025 [October 17th, 2025]
- Novel Machine Learning Model Improves MASLD Detection in Type 2 Diabetes - The American Journal of Managed Care (AJMC) - October 17th, 2025 [October 17th, 2025]
- Hybrid machine learning models for predicting the tensile strength of reinforced concrete incorporating nano-engineered and sustainable supplementary... - October 17th, 2025 [October 17th, 2025]
- Modelling of immune infiltration in prostate cancer treated with HDR-brachytherapy using Raman spectroscopy and machine learning - Nature - October 17th, 2025 [October 17th, 2025]
- Association between atherogenic index of plasma and sepsis in critically ill patients with ischemic stroke: a retrospective cohort study using... - October 17th, 2025 [October 17th, 2025]
- AI enters the nuclear age: Pentagon modernizes warheads with machine learning - Washington Times - October 17th, 2025 [October 17th, 2025]
- AI and Machine Learning - Bentley Systems shares its vision for trustworthy AI - Smart Cities World - October 17th, 2025 [October 17th, 2025]
- Looking back to move forward: can historical clinical trial data and machine learning drive change in participant recruitment in anticipation of... - October 15th, 2025 [October 15th, 2025]
- Physics-Based Machine Learning Paves the Way for Advanced 3D-Printed Materials - Bioengineer.org - October 15th, 2025 [October 15th, 2025]
- Predicting one-year overall survival in patients with AITL using machine learning algorithms: a multicenter study - Nature - October 15th, 2025 [October 15th, 2025]
- Explainable machine learning models for predicting of protein-energy wasting in patients on maintenance haemodialysis - BMC Nephrology - October 15th, 2025 [October 15th, 2025]
- Feasibility of machine learning analysis for the identification of patients with possible primary ciliary dyskinesia - Orphanet Journal of Rare... - October 15th, 2025 [October 15th, 2025]
- Machine learning-based prediction of preeclampsia using first-trimester inflammatory markers and red blood cell indices - BMC Pregnancy and Childbirth - October 15th, 2025 [October 15th, 2025]
- Utilizing AI and machine learning to improve railroad safety: Detecting trespasser hotspots - masstransitmag.com - October 15th, 2025 [October 15th, 2025]
- Precision medicine meets machine learning: AI and oncology biomarkers - pharmaphorum - October 15th, 2025 [October 15th, 2025]
- Aether Pro Exchange Transforms Execution Dynamics with Machine-Learning Optimization - GlobeNewswire - October 15th, 2025 [October 15th, 2025]
- Prevalence, associated factors, and machine learning-based prediction of depression, anxiety, and stress among university students: a cross-sectional... - October 15th, 2025 [October 15th, 2025]
- Artificial Intelligence vs. Machine Learning: Which skills will open better career options in the global - Times of India - October 15th, 2025 [October 15th, 2025]
- Study Reveals Impact of Negative Class Definitions on Machine Learning Accuracy in Immunotherapy - geneonline.com - October 15th, 2025 [October 15th, 2025]
- Muna Al-Khaifi: Detection of Breast Cancer Using Machine Learning and Explainable AI - Oncodaily - October 13th, 2025 [October 13th, 2025]
- Expedia Group Unveils Innovative AI and Machine Learning Solutions to Transform Partner Travel Experiences - Travel And Tour World - October 13th, 2025 [October 13th, 2025]
- Machine Learning-Guided Prediction of Formulation Performance in Inhalable CiprofloxacinBile Acid Dispersions with Antimicrobial and Toxicity... - October 13th, 2025 [October 13th, 2025]
- Machine Learning and BIG DATA workshop planned Oct. 14-15 - West Virginia University - October 11th, 2025 [October 11th, 2025]
- How Google enables third-party circularity by increasing recycling rates with Machine Learning - The World Business Council for Sustainable... - October 11th, 2025 [October 11th, 2025]
- Integrating Artificial Intelligence and Machine Learning in Hydroclimatic Research - A Promising Step Forward - University of Northern British... - October 11th, 2025 [October 11th, 2025]
- Semi-automatic detection of anteriorly displaced temporomandibular joint discs in magnetic resonance images using machine learning - BMC Oral Health - October 11th, 2025 [October 11th, 2025]
- AI and Machine Learning - Partnership to bring infrastructure intelligence to US public sector - Smart Cities World - October 11th, 2025 [October 11th, 2025]
- Between rain and snow, machine learning finds nine precipitation types - Phys.org - October 9th, 2025 [October 9th, 2025]