Microfluidic Chain Reaction
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, microfluidic lab-on-a-chip (also called a micro total analysis system),6,7 which were envisioned pursuant to microelectronic integrated circuits, are generally not integrated on a chip owing to an enduring dependency on cumbersome connections, peripherals, and on computers 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 mesoscopic microfluidic chain reactions (MCRs) based on capillary phenomena for reliable programming and automation of complex liquid handling algorithms integrated in a chip. MCRs are encoded into the chip microarchitecture, 3D printed as a monolithic circuit, and deterministically propagated by the free-energy of a paper pump. With MCR, we sequentially triggered the release of 300 aliquots across chained, interconnected chips, and automated a protocol for SARS-CoV-2 antibodies detection in saliva with visual and quantitative results by cell phone imaging. We automated and miniaturized for the first time the labor-intensive thrombogram with serial and parallel operations including timers and iterative cycles of synchronous flow and stop-flow sequences. Thrombograms with normal, hemophilia-like, and anticoagulant-spiked plasma were generated. MCRs are generalizable, and both the density and number of chain reaction units are scalable. MCRs are untethered from and unencumbered by peripherals, encode programs structurally in situ, and form a 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.050 | 0.022 |
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".