Bibliographic record
Abstract
The ability to efficiently up-convert broadband, low-intensity light would be an enabling technology for background-free biomedical imaging, volumetric 3D printing, and sensitizing silicon focal plane arrays to the short-wave infrared. Our approach uses colloidal quantum dots—size-tunable spin-mixing fluorophores—to absorb low-energy photons and sensitize the spin-triplet excitonic states of nearby conjugated molecules. Once there, pairs of these long-lived excitations can combine via triplet fusion (triplet-triplet annihilation) to generate shorter-wavelength fluorescence. I will discuss our ongoing efforts to harness our recent advances in the synthesis of ultra-small PbS quantum dots (d~1.7 nm, hν_peak,abs=2.2eV) to sensitize ‘red-to-blue’ triplet-fusion upconversion in solution. We show that the long (>µs) photoluminescence lifetimes of these particles enable max-efficiency upconversion at modest light intensities (I_th=220 mW/cm2), overcoming a mildly endothermic sensitization scheme that maximizes the anti-Stokes shift (1.04 eV). This architecture facilitates the photo-initiated polymerization of methylmethacrylate using only long-wavelength light (λ: 637 nm); a demonstration of nanocrystal-sensitized upconversion photochemistry. Finally, from the quasi-equilibrium dynamics of triplet energy transfer, we infer that the chemical potential of photoexcited, ultra-small PbS quantum dots is surprisingly high—completing an advantageous suite of properties for upconversion photochemistry, but reinforcing questions regarding the emissive state.
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 | 0.000 | 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".