CO <sub>2</sub> Capture Using Nitrogen-Doped Porous Carbons Derived from Waste Printed Circuit Boards
Bibliographic record
Abstract
We introduce a novel procedure to synthesize a novel CO 2 adsorbent from waste printed circuit boards. This innovative technique enables the production of nitrogen-rich porous carbon adsorbents at low activation temperatures, ranging from 400 to 500°C, compared to traditional processes that require activation temperatures exceeding 600°C when using KOH. By fine-tuning the activation temperature and modifying the proportion of reactants, namely, NaNH 2, to nonmetallic fractions, it is possible to customize both the pore architecture and the nitrogen levels in the adsorbent, thereby improving its CO 2 adsorption efficiency. The adsorbent, denoted as EN-450-2 (epoxy nitrogen-doped adsorbent activated at 450°C with a weight ratio of 2:1 NaNH 2:electronic waste nonmetal fraction), exhibits a remarkable surface area of 2270 m 2 /g. It demonstrates a CO 2 adsorption capacity of 5.17 mmol/g at 0°C and 1 bar and 3.14 mmol/g at 25°C and 1 bar. Comprehensive analysis indicates that a combination of factors such as pore structure (i.e., narrow micropore, surface area, and total pore volume) influences the CO 2 adsorption performance. At 1 bar pressure and 25°C, EN-450-2 exhibits exceptional CO 2 /N 2 selectivity, moderate isosteric heat of adsorption, rapid adsorption kinetics, substantial dynamic CO 2 capture capacity, and enduring regeneration over five cycles. This work not only provides a sustainable solution to e-waste management but also contributes to global efforts in combating climate change through improved CO 2 capture.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".