Bibliographic record
Abstract
Purpose: Current DNA detection techniques require complicated procedures, specialized training, expensive equipment, invasive samples and significant amount of sample collection and processing time. The purpose of this research was to develop a rapid, accurate, non-invasive and electronic method of DNA sensing that harnesses natural unwinding properties of DNA helicase by attaching it to Carbon Nanotubes. Methods: a. A literature review on methods of attaching proteins to carbon nanotubes was conducted b. A design of the biosensor was developed based on previously reported attachment methods for other proteins c. A part of the sensor was developed by attaching DNA helicase to carbon nanotubes d. The result was tested for preservation of helicase functionality and carbon nanotube electronic structure integrity Results: a. Helicase was successfully attached to carbon nanotubes b. Helicase was found to retain its NTP hydrolysis function, DNA binding and DNA unwinding ability upon attachment c. Carbon nanotube electronic structure and function was not compromised upon attachment Conclusions: Non-specific attachment of helicase to carbon nanotubes preserves enzyme structure and function, allowing rapid DNA unwinding at an in vitro rate comparable to DNA helicase.
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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".