TMB<sup>+</sup>-mediated etching of urchin-like gold nanostructures for colorimetric sensing
Bibliographic record
Abstract
Abstract The morphology-dependent localized surface plasmon resonance of gold nanostructures has been widely utilized for designing sensors. One method relies on the color change of gold nanoparticles upon etching. In previous work, TMB2+ oxidized from 3,3′,5,5′-tetramethylbenzidine (TMB) was found to etch gold nanorods (AuNRs), leading to a spectrum of different colors. However, the preparation of TMB2+ needs the addition of a strong acid and other harsh conditions. Herein, a new colorimetric biosensing platform was developed using urchin-like gold nanoparticles (AuNUs). Compared with AuNRs, the etching of AuNUs can happen under mild conditions by TMB+ at pH 6, protecting enzymes and proteins from denaturation. The role of CTAB surfactant was dissected, and its bromide ions were found to be involved in the etching process. Based on these observations, a one-step colorimetric detection of H2O2 was realized by using horseradish peroxidase and H2O2 to oxidize TMB. Within 30 min, this system achieved a detection limit of 80 nM H2O2. This work offered fundamental insights into the etching of anisotropic gold nanostructures and optimized the etching conditions. These advancements hold promise for broader applications in biosensing and analytical chemistry.
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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".