Rigid and Elastic Microparticles Detection Using 3-D Suspended Polymeric Microfluidics (SPMF<sup>3</sup>) Sensor
Bibliographic record
Abstract
Microsystems have gathered great interests and shown valuable results in the study of mechanobiology and biophysical analysis of cells due to their properties such as microsystems match cell dimensions, provide growth microenvironment such as in-vivo environment, and facilitate parallel analysis that can be done on cells through integration of other sensors or chemicals at low cost and with low amount of sample use. There are a variety of microsystems that measure cell properties such as physical, mechanical, and chemical. In this paper, a new 3-D suspended polymeric microfluidics (SPMF <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) platform for microparticles detection is introduced and tested. The principle of the SPMF <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> is based on bending of structure due to flow forces applied to the microcantilever which is modified when microparticles are passing through the suspended microfluidics. The SPMF <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> is less complex and less expensive compared with the other optical, microfluidics, and microcantilever based techniques formerly employed for microparticles and cells. According to the experimental results, the SPMF <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> is highly sensitive to the particles passing through the micro-nozzle without employing an external exciter. One can study and obtain the biophysical and elastic properties of the passing particles such as size, number, and viscoelasticity with the deflection behavior of the microcantilever.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".