Microthermofluidic systems: From conceptualization to implementation for point of care diagnostic applications
Bibliographic record
Abstract
In recent years, there has been diligent expansion with emerging technological trends in developing microthermofluidic systems. Basically, a microthermofluidic system originates from integrating microfluidic technology and a thermal management system onto a single platform for accurate and stable heat transfer within the microchannel for several biomedical, pharmaceutical, and biochemical applications. In microfluidic devices , temperature is anelementary parameter. Still, it is sporadically ignored because of the non-uniform distribution of heat, high thermal dissipation , and the challenges associated with conventional heaters being able to be integrated into a microfluidic channel . The existing heaters are large, consume more power, are hard to incorporate advanced technological trends and fail to be used for point-of-care testing. Herein, the microthermofluidic system plays an incredible role in a microscale environment that manipulates a minimal fluid volume and offers the desired temperature uniformly on-chip. The microthermofluidic system can be accomplished for countless universal applications in healthcare, food processing, agriculture, chemical engineering , drug delivery, and clinical settings. This article comprehensively discusses the evolution and role of microthermofluidic systems, the significance of material selection, geometric design with appropriate optimization, and different fabrication tools involved in developing integrated microthermofluidic systems that undergo numerous biological and biochemical analyses. Further, the article sheds light on recent advances in microthermofluidic systems that have been implemented and are used for several applications. It also describes miniaturized thermoelectric devices.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".