Activated Carbon Materials for Hydrogen Storage - CSIC
水素貯蔵用活性炭材料 - CSIC (AI 翻訳)
Consejo Superior de Investigaciones Científicas (CSIC), Nanolayers
🤖 gxceed AI 要約
日本語
本データセットは、MAST3RBoostプロジェクトで合成された53種類の超高表面積活性炭サンプルを含む。各サンプルについて、前駆体組成、実験条件、窒素吸着等温線分析、水素吸蔵量（重量・体積基準）が記載されており、機械学習モデルの入力と出力として設計されている。水素貯蔵材料の最適化に有用。
English
This dataset contains 53 ultraporous activated carbon samples from the MAST3RBoost project, with composition, conditions, nitrogen adsorption analysis, and hydrogen uptake measurements. Designed for machine learning input-output modeling to optimize hydrogen storage materials.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
水素は日本のGX戦略の柱の一つであり、高効率な水素貯蔵材料の開発は重要課題。本データセットは機械学習を用いた材料探索を促進し、日本企業・研究機関の水素サプライチェーン構築に貢献し得る。
In the global GX context
Hydrogen is central to global decarbonization. This dataset enables machine learning for novel hydrogen storage materials, supporting the hydrogen economy and complementing ISSB-aligned disclosure on clean energy R&D.
👥 読者別の含意
🔬研究者:For materials scientists and ML researchers: a ready-to-use dataset of activated carbon properties and hydrogen uptake, ideal for developing predictive models.
🏢実務担当者:For companies in hydrogen storage or carbon materials: this dataset can guide R&D toward higher-performance adsorbents.
📄 Abstract（原文）
This datasets includes 53 ultraporous activated carbon samples made in CSIC in the course of the MAST3RBoost project. Each row describes one sample, and the column provide composition information, experimental conditions, and characterisation measurements. Column headers are mostly self-explanatory. Additionally, each header is prefixed by “[input]” if the column describes the precursor mixture composition such as elemental ratios or reaction conditions such as reaction temperature and duration. Conversely, the “[output]” is prepended to columns that provide characterisation quantities for the resulting AC material. These labels also indicate which quantities are supposed to be used as input for machine-learning models, and which are sensible performance metrics they should predict in their output. The output columns can be grouped into two categories. The first group lists the analysis results of nitrogen adsorption isotherms, including details of the isotherm fitting results, and pore size and volume estimates. The latter group provides the actual gravimetric and volumetric hydrogen uptake measurements.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.5281/zenodo.20772052first seen 2026-06-24 04:57:56 · last seen 2026-06-24 04:57:57
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