Ordered Responsive Materials for Sensing Applications
Bibliographic record
Abstract
Detection of small molecules, macromolecules, and biomolecules is of utmost importance for protecting human health and ensuring our well-being. Therefore, a tremendous amount of research goes into the development of novel sensing motifs with increased sensitivity and selectivity to analytes, with very short analysis times, and ease of usability. Of these technologies, 1D, 2D and 3D ordered materials receive a lot of attention due to low cost and visual color change in presence of analytes. These materials were composed of a close packed array of particles or nanocavities, which are capable of interacting with wavelengths of light in the visible region. These interactions lead to constructive and destructive interference of the light in the assembly, resulting in specific wavelengths of light being reflected. Analytes cause shrinking or swelling of these materials and a concomitant change in the critical dimensions of the materials optical components, yielding color changes. This is very convenient, as the naked eye can be used as the “detector”. In this review, we cover many examples of 1D, 2D and 3D ordered materials for sensing applications, ending with examples of other significant sensing technologies. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.016 |
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".