Fluorinated Boron Nitride Quantum Dots: A New 0D Material for Energy Conversion and Detection of Cellular Metabolism
Bibliographic record
Abstract
Abstract Quantum dots encompass a broad spectrum of optical, catalytic, and electrochemical properties bringing in novel applications in catalysis, imaging, displays, and optoelectronics. Herein, the unanticipated broad‐spectrum light absorption and high fluorescence quantum yield in fluorinated boron nitride (FBN) quantum dots are discussed. A heterostructure of FBN quantum dots with a wide‐bandgap semiconductor, titania nanotube arrays, exhibits high photocatalytic activity as evidenced by high external quantum efficiency extending from ultraviolet to green region of the solar spectrum (≈24% at 400 nm). The high activity is confirmed using photoelectrochemical hydrogen evolution experiments. Further, it is demonstrated that high fluorescence quantum yield could be tapped for the detection of glycolytic activity in cancer cells compared to normal cells. This finding could shift the paradigm of molecular detection using quantum dots. The 0D structure and the gap states introduced through fluorination are believed to be responsible for these unprecedented characteristics of boron nitride.
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".