Lanthanide-Encoded Multi-functional Tetrahedral DNA for Precise Nanodevice Encoding
Bibliographic record
Abstract
The encoding of precise nanodevices is undoubtedly an extremely optimal approach for information storage and multiplex detection. Undeniably, precise control over the nanostructure, composition, and morphology of these devices is of paramount importance. However, most of the tags currently used for encoding are limited by insufficient quantity and low resolution, resulting in deficiencies in accuracy, scalability, and exclusivity of the encoded structures. Here, a series of lanthanide-encoded tetrahedral DNA nanodevices are crafted as unique elemental mass spectrometry-encoded tags. These devices combine the multicomponent interference-free detection capability of elemental encoding with the spatially addressable features of DNA nanostructures. After embedding one to four distinct lanthanide tags (LnTs) and arranging them in equal stoichiometric ratios on different DNA tetrahedral frame cantilevers, the lanthanide nanotags transform into dynamic nanoprobes through combination and fine-tuning. The device can function as a 15-component element tag and generate seven signal outputs. It can respond to three different stimuli when targeting multiple objects simultaneously and is then fed into a semiquantitative analysis based on the isotope dilution method. These DNA nanodevices show strong potential for integration with biological circuits, enabling programmable signal release from their three-dimensional architecture, thereby facilitating even more sophisticated biological identification and logical output.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".