Tailoring CO<sub>2</sub> Reduction with Doped Silicon Nanocrystals
Bibliographic record
Abstract
Abstract More than 20 gigatonnes of carbon dioxide are released into the atmosphere every year. The conversion of CO2 into value‐added chemicals and fuels by solar energy is an immediate solution to mitigate CO2 emissions, while providing global energy security. In this work, boron‐ and phosphorus‐doped silicon nanocrystals (ncSi), comprised of three earth‐abundant elements, are investigated for gas‐phase heterogeneous photoreduction of CO2 for the first time. Surface dopants are demonstrated to induce CO2 adsorption capacity. Remarkably, phosphorus‐doped ncSi is found to be the best performer among the singly doped and co‐doped ncSi samples, doubling the rate of pristine ncSi. The enhancement of activity is attributed to the combination of the number of surface hydrides, its surface hydrophobicity, the addition of electronegative surface atoms, and perhaps an enhanced hydridic character of the SiH induced by the n‐doping effect. Significantly, boron and phosphorus dopants are shown to provide increased stability of CO2 reduction activity compared to pristine ncSi after storing the samples in air for 2 weeks. These noteworthy findings open up a pathway to develop sustainable alternatives for existing photocatalysts for CO2 conversion.
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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".