ArticleGels (Basel, Switzerland)2025
Machine Learning Models for Predicting Thermal Properties of Radiative Cooling Aerogels.
Article in Gels (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Multilayer Composite Structured Transparent Infrared-Selective Stealth Films with Synergistic Radiative Cooling.Nanomaterials (Basel, Switzerland) · 2026Article
- Digital Twin-Driven Optimization of Pilot-Scale Polyurethane Aerogel Production Using SVR Modelling.Gels (Basel, Switzerland) · 2026Article
- Thermal Conductivity in Nanoporous Aerogels: A Critical Review of Gas and Solid Conduction Models and Structure-Property Relations.Gels (Basel, Switzerland) · 2026Review
- Design strategies, manufacturing, and applications of radiative cooling technologies.Nanophotonics (Berlin, Germany) · 2025Review
- Interpretable Machine Learning Analysis of Design Factors in Hydrogel Supercapacitors.Gels (Basel, Switzerland) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
The escalating global climate crisis and energy challenges have made the development of efficient radiative cooling materials increasingly urgent. This study presents a machine-learning-based model for predicting the performance of radiative cooling aerogels (RCAs). The model integrated multiple parameters, including the material composition (matrix material type and proportions), modification design (modifier type and content), optical properties (solar reflectance and infrared emissivity), and environmental factors (solar irradiance and ambient temperature) to achieve accurate cooling performance predictions. A comparative analysis of various machine learning algorithms revealed that an optimized XGBoost model demonstrated superior predictive performance, achieving an R
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.