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関連する概念動画

Adsorption of Gases on Solids01:28

Adsorption of Gases on Solids

18
Adsorption is a process where molecules, known as the adsorbates, accumulate on a surface, which is referred to as the adsorbent or substrate. Occurring at the solid-gas interface, this phenomenon is crucial in various scientific and industrial contexts. The reverse of adsorption is desorption.Two types of adsorptions exist: physical (physisorption) and chemical (chemisorption). Physisorption involves gas molecules held to the solid's surface by relatively weak intermolecular van der Waals...
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Adsorption Isotherms II01:25

Adsorption Isotherms II

15
Brunauer, Emmett, and Teller (BET) introduced a theory in 1938 that modified Langmuir's assumptions to explain multilayer physical adsorption. This theory is applicable to Type II isotherms and provides a more realistic picture of adsorption processes. The BET theory assumes a uniform solid surface with localized adsorption sites, where adsorption at one site doesn't affect adsorption at neighboring sites. This theory also allows for the possibility of additional molecules being adsorbed on top...
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Adsorption Isotherms I01:29

Adsorption Isotherms I

10
Adsorption isotherms are mathematical models that describe how molecules in a gas or liquid phase interact with surfaces. Two of the most common isotherm models are the Langmuir and Freundlich isotherms, which relate to Type I monolayer chemisorption. The Langmuir model is based on four key assumptions:• Adsorption cannot exceed monolayer coverage.• All surface sites are equivalent.• Molecules adsorb only at vacant sites.• There are no interactions between adsorbed...
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Analyte Adsorption and Distribution01:09

Analyte Adsorption and Distribution

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In certain chromatographic separations, solutes transfer between the mobile phase and the stationary phase via sorption, which typically refers to the process of adsorption. For many chromatographic systems, the sorption process often depends on the polarity of the compounds—an expression of the overall dipole moment within the molecule. During the separation process, there is competition between the solute and solvent for adsorption to the stationary phase. Highly polar compounds and...
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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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VSEPR Theory for Determination of Electron Pair Geometries
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関連する実験動画

Updated: Mar 2, 2026

Two-way Valorization of Blast Furnace Slag: Synthesis of Precipitated Calcium Carbonate and Zeolitic Heavy Metal Adsorbent
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マグネシアベース吸着材への二酸化炭素吸着を予測する解釈可能なアンサンブル機械学習モデル

Lin Fan1, Yan He2, Yunfeng Mo3

  • 1Engineering Training Centre, Liaoning Petrochemical University, Fushun, 113001, Liaoning, China; School of Mechanical Engineering, Shenyang University of Technology, Shenyang 110870, Liaoning, China.

Environmental research
|February 28, 2026
PubMed
まとめ

二酸化炭素（CO2）の酸化マグネシウム（MgO）への吸着予測は、炭素回収に不可欠である。本研究では、機械学習を用いて正確な予測モデルを構築し、材料形態とプロセス条件が重要な要因であることを特定する。

キーワード:
CO(2)吸着アンサンブル学習MgOベース吸着材モデル解釈Streamlit

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Adsorption Device Based on a Langatate Crystal Microbalance for High Temperature High Pressure Gas Adsorption in Zeolite H-ZSM-5
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科学分野:

  • 材料科学; 化学工学; データサイエンス

背景:

  • 効果的な炭素回収技術の開発には、二酸化炭素（CO2）吸着の正確な予測が不可欠です。; 酸化マグネシウム（MgO）ベースの吸着材はCO2回収に有望ですが、性能を最適化するには予測モデルが必要です。

研究 の 目的:

  • MgOベース吸着材のCO2吸着容量を予測するための解釈可能な機械学習フレームワークを開発すること。; 吸着容量に影響を与える主要な材料およびプロセスパラメータを特定すること。; 改良されたCO2吸着材を設計するためのツールを提供すること。

主な方法:

  • 14の候補機械学習モデルを評価しました。; Optunaを用いたベイズハイパーパラメータ最適化のためにトップモデルを選択しました。; エントロピー重み付け法とTOPSISを統合した重み付きアンサンブルモデルを構築しました。; 特徴量重要度分析のためにSHapley Additive exPlanations（SHAP）を利用しました。

主要な成果:

  • 最適化されたアンサンブルモデルは、独立したテストセットでR2値0.9903を達成しました。; 材料形態、特に粒状が最も重要な特徴でした（寄与率43.8%）。; 時間（23.3%）や温度（14.1%）などのプロセスパラメータも主要な寄与要因でした。; 最適な吸着条件は、中程度の温度（200-400°C）および吸着時間（100-250分）として特定されました。

結論:

  • 開発された機械学習フレームワークは、MgOベース吸着材へのCO2吸着に対して正確な予測を提供します。; 材料形態とプロセスパラメータは吸着容量に大きく影響します。; 本研究は、高性能MgOベースCO2吸着材の設計のためのメカニズム的洞察と、リアルタイム予測のための展開可能なWebアプリケーションを提供します。