2D/0D Heterojunction Fluorescent Probe with Schottky Barrier Based on Ti<sub>3</sub>C<sub>2</sub>T<sub>X</sub> MXene Loaded Graphene Quantum Dots for Detection of H<sub>2</sub>S During Food Spoilage
Bibliographic record
Abstract
Abstract Hydrogen sulfide (H2S) contamination of food has raised widespread public health concerns, leading to substantial medical and economic burdens. Herein, a 2D/0D heterojunction fluorescent probe (TCTG) with Schottky barriers (SB) is designed and synthesized, utilizing Ti3C2Tx MXene‐loaded graphene quantum dots (GQDs), for the detection of H2S during food spoilage. The microstructures observed through SEM and TEM reveal that uniformly sized GQDs are evenly attached to the surface of a monolayer Ti3C2Tx. The chemisorption between GQDs and Ti3C2Tx facilitates charge transfer and the formation of SB, resulting in intramolecular charge transfer (ICT) effects. With the introduction of H2S, TCTG(50%) exhibits the highest sensitivity, selectivity, and anti‐interference properties, with ultra‐fast fluorescence transient reaction (3s) and remarkably low detection limit of 41.82 ppb as well as noticeable color change. When TCTG(50%) reacted with H2S, the ICT effects are inhibited, leading to the recovery of photoinduced electron transfer (PET) and fluorescence quenching. Notably, probe TCTG is effectively utilized to detect changes in H2S levels in raw foods to assess their quality. Overall, the significance of this study is its potential to revolutionize food spoilage detection, offering a fast, reliable, and sensitive method to ensure food safety and reduce associated health and economic burdens.
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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 | 0.001 | 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".