Recent Progress in Nanotechnology for COVID-19 Prevention, Diagnostics and Treatment
Bibliographic record
Abstract
The COVID-19 pandemic is currently an unprecedented public health threat. The rapid spread of infections has led to calls for alternative approaches to combat the virus. Nanotechnology is taking root against SARS-CoV-2 through prevention, diagnostics and treatment of infections. In light of the escalating demand for managing the pandemic, a comprehensive review that highlights the role of nanomaterials in the response to the pandemic is highly desirable. This review article comprehensively discusses the use of nanotechnology for COVID-19 based on three main categories: prevention, diagnostics and treatment. We first highlight the use of various nanomaterials including metal nanoparticles, carbon-based nanoparticles and magnetic nanoparticles for COVID-19. We critically review the benefits of nanomaterials along with their applications in personal protective equipment, vaccine development, diagnostic device fabrication and therapeutic approaches. The remaining key challenges and future directions of nanomaterials for COVID-19 are briefly discussed. This review is very informative and helpful in providing guidance for developing nanomaterial-based products to fight against COVID-19.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".