Modulating Hydrogen Exchange Capabilities by Heterogenizing Pd Nanoclusters onto Ni <sub>3</sub> C Multipods for Efficiently Driving the “Formaldehyde-Nitrate” Tandem Electrochemical System
Bibliographic record
Abstract
Nitrate and formaldehyde, common industrial byproducts and waterborne pollutants, pose serious environmental and health hazards, yet their efficient conversion remains challenging due to sluggish hydrogen (H*) transfer and limited recycling strategies. While recent studies have explored nitrate reduction (NO 3 RR) and formaldehyde oxidation (FOR) coupling, they faced critical limitations such as no H 2 generation, a lack of electricity output, and reliance on Cu-based catalysts prone to deactivation. This work presents Pd nanoclusters on nickel carbide (Pd nc -Ni 3 C) that serve as a noncopper bifunctional catalyst that possesses superior H* exchange capabilities enabling dual-directional catalysis of NO 3 RR and FOR. At the cathode, Pd nc -Ni 3 C achieves an onset potential of +0.27 V vs RHE, and 98% Faradaic efficiency for NH 3 at −0.3 V. At the anode, Pd nc -Ni 3 C achieves an efficient FOR at a low onset potential of 0.04 V and enables a broad oxidation window (0–1.2 V) with high current density (up to 910 mA cm –2 ), outperforming previously reported Cu- and Ni-based systems. Differential electrochemical mass spectra reveal a previously unexplored intermolecular coupling pathway for H 2 evolution, advancing mechanistic insight into the 1-electron formaldehyde oxidation process. By coupling the NO 3 RR and FOR, a high-performance “Formaldehyde–Nitrate” galvanic cell is achieved with an OCV of 0.88 V and peak power density of 7.4 mW cm –2 . Distinctively, this Ni based system simultaneously converts industrial waste into green energy carriers (H 2, NH 3 ) and value-added chemicals (formate) while producing electricity, offering both environmental and economic benefits.
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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".