A low-cost, label-free DNA detection method in lab-on-chip format based on electrohydrodynamic instabilities, with application to long-range PCR
Bibliographic record
Abstract
In order to evolve from a "chip in the lab" to a "lab on a chip" paradigm, there is still a strong demand for low-cost, portable detection technologies, notably for analytes at low concentrations. Here we report a new label-free DNA detection method with direct electronic read, and apply it to long-range PCR. This method uses a nonlinear electrohydrodynamic phenomenon: when subjected to high electric fields (typically above 100 V cm(-1)), suspensions of large polyelectrolytes, such as long DNA molecules, create "giant" dynamic concentration fluctuations. These fluctuations are associated with large conductivity inhomogeneities, and we use here a contact-mode local conductivity detector to detect these fluctuations. In order to decouple the detection electronics from the high voltage excitation one, an original "doubly symmetric" floating mode battery-operated detection scheme was developed. A wavelet analysis was then applied, to unravel from the chaotic character of the electohydrodynamic instabilities a scalar signal robustly reflecting the amplification of DNA. As a first proof of concept, we measured the products of the off-chip amplification of 10 kbp DNA from lambda phage DNA, achieving a sensitivity better than 100 fg DNA in the original 50 μl sample. This corresponds to the amplification products of less than 100 initial copies of target DNA. The companion enabling technologies developed to implement this new concept, i.e. the doubly symmetric contact conductivity detection and wavelet analysis, may also find various other applications in lab-on-chips.
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 | 0.001 | 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 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".