Clog-free cell filtration using resettable cell traps
Bibliographic record
Abstract
The separation of cells by filtration through microstructured constrictions is limited by clogging and adsorption, which reduce selectivity and prevent the extraction of separated cells. To address this key challenge, we developed a mechanism for simply and reliably adjusting the cross-section of a microfluidic channel to selectively capture cells based on a combination of size and deformability. After a brief holding period, trapped cells can then be released back into flow, and if necessary, extracted for subsequent analysis. Periodically clearing filter constrictions of separated cells greatly improves selectivity and throughput, and minimizes adsorption of cells to the filter microstructure. This mechanism is capable of discriminating cell-sized polystyrene microspheres with <1 μm resolution. Rare cancer cells doped into leukocytes can be enriched ~1800× with ~90% yield despite a significant overlap in size between these cell types. An important characteristic of this process is that contaminant leukocytes are captured by non-specific adsorption and not mechanical constraint, enabling repeated filtration to improve performance. The throughput of this mechanism is 900,000 cells per hour for 32 multiplexed microchannels, or ~1,200,000 cells cm⁻² h⁻¹ on a per area basis, which exceeds existing micropore filtration mechanisms by a factor of 20.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".