Low dimensional materials for glucose sensing
Bibliographic record
Abstract
Biosensors are essential components for effective healthcare management. Since biological processes occur on molecular scales, nanomaterials and nanosensors intrinsically provide the most appropriate landscapes for developing biosensors. Low-dimensional materials have the advantage of offering high surface areas, increased reactivity and unique physicochemical properties for efficient and selective biosensing. So far, nanomaterials and nanodevices have offered significant prospects for glucose sensing. Targeted glucose biosensing using such low-dimensional materials enables much more effective monitoring of blood glucose levels, thus providing significantly better predictive diabetes diagnostics and management. In this review, recent advances in using low dimensional materials for sensing glucose are summarized. Sensing fundamentals are discussed, as well as invasive, minimally-invasive and non-invasive sensing methods. The effects of morphological characteristics and size-dependent properties of low dimensional materials are explored for glucose sensing, and the key performance parameters such as selectivity, stability and sensitivity are also discussed. Finally, the challenges and future opportunities that low dimensional materials can offer for glucose sensing are outlined.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".