Microfluidic Chain Reaction: Conditional, Structurally Programmed Propagation of Capillary Flow Events
Bibliographic record
Abstract
Chain reactions are characterized by initiation, propagation and termination, are stochastic at microscopic scales and underlie vital chemical (e.g. combustion engines), nuclear and biotechnological (e.g. polymerase chain reaction) applications.1-5 At macroscopic scales, chain reactions are deterministic and limited to applications for entertainment and art such as falling domino and Rube Goldberg machines. Appositely, the microfluidic lab-on-a-chips (also called a micro total analysis system),6,7 was envisioned as an integrated chip, akin to microelectronic integrated circuits, yet in practice remain dependent on cumbersome peripherals, connections, and a computer for automation.8-11 Capillary microfluidics integrate energy supply and flow control onto a single chip by using capillary phenomena, but programmability remains rudimentary with at most a handful (eight) operations possible.12-19 Here we introduce the microfluidic chain reaction (MCR) as the conditional, structurally programmed propagation of capillary flow events. Monolithic chips integrating a MCR are 3D printed, and powered by the free-energy of a paper pump, autonomously execute liquid handling algorithms step-by-step. With MCR, we automated (i) the sequential release of 300 aliquots across chained, interconnected chips, (ii) a protocol for SARS-CoV-2 antibodies detection in saliva, and (iii) a thrombin generation assay by continuous subsampling and analysis of coagulation-activated plasma with parallel operations including timers, iterative cycles of synchronous flow and stop-flow operations. MCRs are untethered from and unencumbered by peripherals, encode programs structurally in situ, and can form frugal, versatile, bona fide lab-on-a-chip with wide-ranging applications in liquid handling and point-of-care diagnostics.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".