ArticleChemical science2021
Machine-learning-accelerated multimodal characterization and multiobjective design optimization of natural porous materials.
Article in Chemical science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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.
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Who cites it
2 citing papers in PubMed, 19 citations in OpenAlex.
- Machine Learning in the Development of Adsorbents for Clean Energy Application and Greenhouse Gas Capture.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022Review
- Multimedia Character Modeling Design and Modeling of Cartoon Animation Based on Bayesian Sequence Recommendation Algorithm.Computational intelligence and neuroscience · 2022Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Natural porous materials such as nanoporous clays are used as green and low-cost adsorbents and catalysts. The key factors determining their performance in these applications are the pore morphology and surface activity, which are typically represented by properties such as specific surface area, pore volume, micropore content and pH. The latter may be modified and tuned to specific applications through material processing and/or chemical treatment. Characterization of the material, raw or processed, is typically performed experimentally, which can become costly especially in the context of tuning of the properties towards specific application requirements and needing numerous experiments. In this work, we present an application of tree-based machine learning methods trained on experimental datasets to accelerate the characterization of natural porous materials. The resulting models allow reliable prediction of the outcomes of experimental characterization of processed materials (
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Registered trials
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