Enhancement in the limit of detection of lab‐on‐chip microfluidic devices using functional nanomaterials
Bibliographic record
Abstract
Abstract Technological developments in recent years have witnessed a paradigm shift towards lab‐on‐chip devices for various diagnostic applications. Lab‐on‐chip technology integrates several functions typically performed in a large‐scale analytical laboratory on a small‐scale platform. These devices are more than the miniaturized versions of conventional analytical and diagnostic techniques. The advances in fabrication techniques, material sciences, surface modification strategies, and their integration with microfluidics and chemical and biological‐based detection mechanisms have enormously enhanced the capabilities of these devices. The minuscule sample and reagent requirements, capillary‐driven pump‐free flows, faster transport phenomena, and ease of integration with various signal readout mechanisms make these platforms apt for use in resource‐limited settings, especially in developing and underdeveloped parts of the world. The microfluidic lab‐on‐a‐chip technology offers a promising approach to developing cost‐effective and sustainable point‐of‐care testing applications. Numerous merits of this technology have attracted the attention of researchers to develop low‐cost and rapid diagnostic platforms in human healthcare, veterinary medicine, food quality testing, and environmental monitoring. However, one of the major challenges associated with these devices is their limited sensitivity or the limit of detection. The use of functional nanomaterials in lab‐on‐chip microfluidic devices can improve the limit of detection by enhancing the signal‐to‐noise ratio, increasing the capture efficiency, and providing capabilities for devising novel detection schemes. This review presents an overview of state‐of‐the‐art techniques for integrating functional nanomaterials with microfluidic devices and discusses the potential applications of these devices in various fields.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".