Pd Single‐Atoms Doped Cu <sub>3</sub> P Quantum Dots with Moderately Optimized H* Sorption Behaviors for Actualizing the Multifunctional “Formaldehyde‐Nitrate” Galvanic System
Bibliographic record
Abstract
Abstract Nitrate and formaldehyde, which are substantial wastes in industrial and agricultural effluents, pose significant hazards to the human health and ecosystem. Current purification technologies remain great challenges due to the unsatisfactory energy‐intensive, time‐consuming and noticeably costly reasons. Herein, a bifunctional electrocatalysts of Pd single‐atoms doped Cu 3 P quantum dots (Pd‐Cu 3 P SA‐QDs) are reported to moderately optimize the H* sorption behaviors for accelerating the kinetics of nitrate reduction (NO 3 RR) and formaldehyde oxidation (FOR) reactions in a dual‐directional way, thus realizing the high activity and selectivity for both cathodic ammonia (NH 3 ) synthesis and anodic H 2 production concurrently. Specifically, a very positive onset potential of +0.36 V (versus RHE) is recorded for NO 3 RR with a high faradaic efficiency (FE) of 99 % for NH 3 at −0.3V (versus RHE), while the FOR can be initialized at a very low onset potential of −0.07 V (versus RHE) with > 90 % FE for formate and > 95 % FE for H 2 at 0.3 V (versus RHE). Notably, a self‐powered galvanic system can be triggered by integrating the above two electrode‐based reactions, thus exhibiting an open‐circuit voltage of 0.892 V, a peak power density of 12.1 mW cm −2 , and capable of generating 8.8 KWh kg −1 of electrical energy.
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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".