High‐throughput and sensitive particle counting by a novel microfluidic differential resistive pulse sensor with multidetecting channels and a common reference channel
Bibliographic record
Abstract
High-throughput particle counting by a differential resistive pulse sensing method in a microfluidic chip is presented in this paper. A sensitive differential microfluidic sensor with multiple detecting channels and one common reference channel was devised. To test the particle counting performance of this chip, an experimental system which consists of the microfluidic chip, electric resistors, an amplification circuit, a LabView based data acquisition device was developed. The influence of the common reference channel on the S/N of particle detection was investigated. The relationship between the hydraulic pressure drop applied across the detecting channel and the counting throughput was experimentally obtained. The experimental results show that the reference channel designed in this work can improve the S/N by ten times, thus enabling sensitive high-throughput particle counting. Because of the greatly improved S/N, the sensing gate with a size of 25 × 50 × 10 μm (W × L × H) in our chips can detect and count particles larger than 1.5 μm in diameter. The counting throughput increases with the increase in the flowing velocity of the sample solution. An average throughput of 7140/min under a flow rate of 10 μL/min was achieved. Comparing with other methods, the structure of the chip and particle detecting mechanism reported in this paper is simple and sensitive, and does not have the crosstalking problem. Counting throughput can be adjusted simply by changing the number of the detecting channels.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".