Biophotonic logic devices based on quantum dots and temporally-staggered Förster energy transfer relays
Bibliographic record
Abstract
Integrating photonic inputs/outputs into unimolecular logic devices can provide significantly increased functional complexity and the ability to expand the repertoire of available operations. Here, we build upon a system previously utilized for biosensing to assemble and prototype several increasingly sophisticated biophotonic logic devices that function based upon multistep Förster resonance energy transfer (FRET) relays. The core system combines a central semiconductor quantum dot (QD) nanoplatform with a long-lifetime Tb complex FRET donor and a near-IR organic fluorophore acceptor; the latter acts as two unique inputs for the QD-based device. The Tb complex allows for a form of temporal memory by providing unique access to a time-delayed modality as an alternate output which significantly increases the inherent computing options. Altering the device by controlling the configuration parameters with biologically based self-assembly provides input control while monitoring changes in emission output of all participants, in both a spectral and temporal-dependent manner, gives rise to two input, single output Boolean Logic operations including OR, AND, INHIBIT, XOR, NOR, NAND, along with the possibility of gate transitions. Incorporation of an enzymatic cleavage step provides for a set-reset function that can be implemented repeatedly with the same building blocks and is demonstrated with single input, single output YES and NOT gates. Potential applications for these devices are discussed in the context of their constituent parts and the richness of available signal.
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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".