Time-Gated FRET and DNA-Based Photonic Molecular Logic Gates: AND, OR, NAND, and NOR
Bibliographic record
Abstract
Molecular logic devices (MLDs) constructed from DNA are promising for applications in bioanalysis, computing, and other applications requiring Boolean logic. These MLDs accept oligonucleotide inputs and generate fluorescence output through changes in structure. Although fluorescent dyes are most common in MLD designs, nontraditional luminescent materials with unique optical properties can potentially enhance MLD capabilities. In this context, luminescent lanthanide complexes (LLCs) have been largely overlooked. Here, we demonstrate a set of high-contrast DNA photonic logic gates based on toehold-mediated strand displacement and time-gated FRET. The gates include NAND, NOR, OR, and AND designs that accept two unlabeled target oligonucleotide sequences as inputs. Bright "true" output states utilize time-gated, FRET-sensitized emission from an Alexa Fluor 546 (A546) dye acceptor paired with a luminescent terbium cryptate (Tb) donor. Dark "false" output states are generated through either displacement of the A546, or through competitive and sequential quenching of the Tb or A546 by a dark quencher. Time-gated FRET and the long luminescence lifetime and spectrally narrow emission lines of the Tb donor enable 4-10-fold contrast between Boolean outputs, ≤10% signal variation for a common output, multicolor implementation of two logic gates in parallel, and effective performance in buffer and serum. These metrics exceed those reported for many other logic gate designs with only fluorescent dyes and with other non-LLC materials. Preliminary three-input AND and NAND gates are also demonstrated. The powerful combination of an LLC FRET donor with DNA-based logic gates is anticipated to have many future applications in bioanalysis.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".