Electrothermal regeneration of carbon-modified CuBTC for volatile organic compound adsorption
Bibliographic record
Abstract
Electrothermal regeneration uses the Joule effect to heat adsorbents, enhancing desorption efficiency and reducing energy consumption. This study modifies the metal-organic framework (MOF) CuBTC synthesis for electrothermal regeneration after volatile organic compounds (VOCs) adsorption. Carbon-based materials, carbon nanotubes (CNT), porous carbon, and graphene, were integrated into CuBTC to introduce conductivity. X-ray diffraction (XRD) confirmed the structural integrity of the modified MOFs, while thermogravimetric analysis evaluated thermal stability and carbon content. Electron microscopy showed uniform carbon additive incorporation, and nitrogen and n-heptane adsorption isotherms assessed adsorption properties. Graphene proved most effective in lowering electrical resistivity, followed by CNT and porous carbon. However, CNT and graphene exhibited reduced adsorption performance due to aggregation and poor dispersion. Generally, modified samples had lower surface areas and n-heptane adsorption than unmodified CuBTC. Incorporating 16.7 g/ml porous carbon achieved a resistivity of 4.1 Ω m, with a surface area of 1223 m 2 /g and adsorption properties similar to the original MOF. This suggests potential for efficient adsorption and regeneration. Comparative analyses showed electrothermal regeneration outperformed conventional methods in speed and energy efficiency, advancing the development of conductive MOFs for adsorption and regeneration processes. • Carbon modifiers enable CuBTC's electrothermal regeneration with low resistivity. • Porous carbon increases CuBTC's surface area to 1223 m 2 /g, enhancing adsorption. • Graphene achieves the lowest resistivity (0.5 Ω m) but affects adsorption capacity. • Electrothermal regeneration is faster, using less than half the energy of heating tape. • Improved CNT and graphene dispersion could enhance MOF adsorption and conductivity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".