Manipulating the Optically Active Defect–Defect Interaction of Colloidal Quantum Dots for Carbon Dioxide Photoreduction
Bibliographic record
Abstract
Defect engineering in colloidal quantum dots (QDs), a typical photocatalytic material, is promising to tailor optoelectronic properties and achieve solar-to-fuel energy conversion. However, understanding the effect of defect–defect interactions on both charge carrier and catalytic dynamics is still challenging. Here, we report a class of defect-engineered copper-deficient Zn-doped CuInS 2 (ZCIS) QDs that synergistically utilize copper vacancy and Cu 2+ defect states to realize CO 2 photoreduction. Steady and transient optical characterizations reveal that the density of copper vacancy can manipulate the distribution of optically active Cu + and Cu 2+ defect states (appearing as Cu In ″ and Cu Cu • species, respectively), wherein the Cu + defect states suppress interband absorption and sharpen the Shockley–Read–Hall recombination, while Cu 2+ defect states enable the prolonged exciton lifetime of QDs. In situ infrared spectroscopic investigation and theoretical density functional calculation demonstrate the photoactive Cu 2+ defect states nearby the copper vacancy in ZCIS QDs can effectively activate CO 2 to the COOH* intermediates, leading to a remarkable photocatalytic CO production rate up to 532.3 μmol g –1 h –1 (turnover number ∼1963) after 120 h illumination.
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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) | 0.000 | 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 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".