Energy Level Engineering in Gold Nanoclusters for Exceptionally Bright NIR Electrochemiluminescence at a Low Trigger Potential
Bibliographic record
Abstract
Electrochemiluminescence (ECL) is a widely used light output mechanism from electrochemical excitation. Understanding the intrinsic essence for ideal ECL generation remains a fundamental challenge. Here, based on the molecular orbital theory, we reported an energy level engineering strategy to regulate the ECL performance by using ligand-protected gold nanoclusters (AuNCs) as luminophores and N, N -diisopropylethylamine (DIPEA) as a coreactant. The energy level matching between the AuNCs and DIPEA effectively promoted their electron transfer reactions, thus improving the excitation efficiency and reducing the trigger potential. Simultaneously, the narrow band gap of the AuNCs further enabled enhanced emission efficiency. Using the energy level engineering theory developed here, a dual-enhanced strategy was proposed, and β-CD-AuNCs were designed to further verify this mechanism. The β-CD-AuNCs/DIPEA system resulted in highly stable near-infrared ECL with an unprecedented ECL efficiency (145-fold higher than that of the classic Ru(bpy) 3 2+ /tetra- n -butylammonium perchlorate system) and a low trigger potential of 0.48 V. A visual NIR-ECL based on this ECL system was successfully realized by an infrared camera. This work provides an original mechanistic understanding for designing efficient ECL systems, which promises to be a harbinger for broad applicability of this strategy for other ECL systems and ECL sensing platforms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".