Bibliographic record
Abstract
In recent years, nanomaterials, defined as materials < 100 nm in a single dimension, have garnered significant interest for the development of novel applications in the physical and life sciences. This is especially true for luminescent nanoparticles, which have been investigated for the development of sensors, imaging/diagnostic probes, display and solar cell applications. Recently, a relatively new class of luminescent nanomaterials, namely carbon dots, has come to light. Carbon dots, sometimes known as carbongenic dots, are carbon, oxygen, nitrogen and hydrogen containing materials with the first two elements typically accounting for ~90% of their elemental composition. Moreover, they are typically water dispersible and can be prepared from an abundant number of inexpensive sources including small molecules such as citric acid, amino acids and sugars. While they are small in size (typically 1-10 nm), they can offer a high quantum yield of emission, a process that is controlled through passivation of the surface with an organic reagent. Of particular interest are their optical properties, which can be tailored via careful selection of the starting precursors and the desired synthesis route resulting in the ability to generate fluorescence from the blue to the near infrared regions of the spectrum. In addition to their versatile optical properties, these carbon dots are generally known to have low cytotoxicity and good biocompatibility. Combined with their small size and versatile optical properties, developing carbon dots as a nanoplatform can be achieved as they lend themselves for integration in a myriad of applications most notably in bio-imaging and sensing. Our work focuses on achieving a fundamental understanding of the synthesis of carbon dots in order to control their size and achieve homogenous surface chemistry and optical properties. As they are fluorescent, we have investigated their development as bimodal imaging probes and exploited their temperature and pH dependent optical properties in order develop novel intracellular temperature and pH sensors. We can also endow them with chiral properties during synthesis opening up novel avenues for chiral sensing and antimicrobial applications. Finally, in recent developments, we have exploited their physico-chemical properties in green energy applications focusing on the production of biofuels.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.053 | 0.034 |
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".