A Low-Viscosity Copper-Based Deep Eutectic Solvent for Carbon Monoxide Elimination at the Return Corner of Coal Mining Faces

To address the recurring issue of excessive carbon monoxide (CO) concentrations at the return corner of fully mechanized mining faces under goaf conditions, this study investigated the elimination of CO at ambient temperature and pressure using deep eutectic solvents (DESs). CO, a colorless, odorless, and highly toxic gas, is notoriously difficult to remove under conventional conditions. A series of DESs were prepared and screened, revealing that the ethanol-modified system [Emim]Cl-CuCl-1.0E exhibited optimal CO elimination performance under conditions of 298.15 K and atmospheric pressure. Further investigations measured the viscosity-temperature relationship and thermal stability of this system while systematically examining the effects of temperature, CO contact time, and storage duration on its elimination efficiency. Analysis by FTIR and Raman spectroscopy indicated that Cu(I) ions play a crucial role in the CO absorption process. The introduction of ethanol significantly enhanced the activity of the Cu(I) ions, thereby effectively improving the CO elimination capacity of the system. This study proposes a novel potential method for managing CO in goaf areas and provides an experimental foundation for the application of DESs in the field of gas purification.

Authors

Institutions

Publication Details

Journal
Molecules
Published
2026-02-25
DOI
https://doi.org/10.3390/molecules31050772
Primary Topic
Coal Properties and Utilization
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Low-Viscosity Copper-Based Deep Eutectic Solvent for Carbon Monoxide Elimination at the Return Corner of Coal Mining Faces

Junguang Wang, Weiji Sun, Tianyu Xin, Yashengnan Sun et al.
Molecules
Coal Properties and Utilization
article

A Low-Viscosity Copper-Based Deep Eutectic Solvent for Carbon Monoxide Elimination at the Return Corner of Coal Mining Faces

Junguang Wang, Weiji Sun, Tianyu Xin, Yashengnan Sun, Jiaxu Jin, Xihua Zhou, Gang Bai, Bing Liang
article en

Abstract

To address the recurring issue of excessive carbon monoxide (CO) concentrations at the return corner of fully mechanized mining faces under goaf conditions, this study investigated the elimination of CO at ambient temperature and pressure using deep eutectic solvents (DESs). CO, a colorless, odorless, and highly toxic gas, is notoriously difficult to remove under conventional conditions. A series of DESs were prepared and screened, revealing that the ethanol-modified system [Emim]Cl-CuCl-1.0E exhibited optimal CO elimination performance under conditions of 298.15 K and atmospheric pressure. Further investigations measured the viscosity-temperature relationship and thermal stability of this system while systematically examining the effects of temperature, CO contact time, and storage duration on its elimination efficiency. Analysis by FTIR and Raman spectroscopy indicated that Cu(I) ions play a crucial role in the CO absorption process. The introduction of ethanol significantly enhanced the activity of the Cu(I) ions, thereby effectively improving the CO elimination capacity of the system. This study proposes a novel potential method for managing CO in goaf areas and provides an experimental foundation for the application of DESs in the field of gas purification.

MoleculesVol. 31(5)
Liaoning Technical University (CN)
State Key Laboratory of Coal Mine Disaster Dynamics and Control
Openalex Percentile: Top 46%
Coal Properties and Utilization
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.