Two-Way Photoswitching Using One Type of Near-Infrared Light, Upconverting Nanoparticles, and Changing Only the Light Intensity
Bibliographic record
Abstract
Only one type of lanthanide-doped upconverting nanoparticle (UCNP) is needed to reversibly toggle photoresponsive organic compounds between their two unique optical, electronic, and structural states by modulating merely the intensity of the 980 nm excitation light. This reversible "remote-control" photoswitching employs an excitation wavelength not directly absorbed by the organic chromophores and takes advantage of the fact that designer core-shell-shell NaYF(4) nanoparticles containing Er(3+)/Yb(3+) and Tm(3+)/Yb(3+) ions doped into separate layers change the type of light they emit when the power density of the near-infrared light is increased or decreased. At high power densities, the dominant emissions are ultraviolet and are appropriate to drive the ring-closing, forward reactions of dithienylethene (DTE) photoswitches. The visible light generated from the same core-shell-shell UCNPs at low power densities triggers the reverse, ring-opening reactions and regenerates the original photoisomers. The "remote-control" photoswitching using NIR light is as equally effective as the direct switching with UV and visible light, albeit the reaction rates are slower. This technology offers a highly convenient and versatile method to spatially and temporally regulate photochemical reactions using a single light source and changing either its power or its focal point.
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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 | 0.001 |
| 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".