Microenvironments as an Explanation for the Mismatch between Photochemical Absorptivity and Reactivity
Bibliographic record
Abstract
Photochemistry is at the forefront of many modern technologies, from additive manufacturing to phototherapeutics to sun protection and organic synthesis. It is commonly believed that an absorbance spectrum, showing the likelihood of a photon to be absorbed by a chromophore at a given wavelength, is an accurate predictor of how well a photochemical process will proceed when irradiated with different colors of light. Over the past decade this paradigm has been repeatedly challenged for many photochemical systems, as a distinct mismatch between the absorption spectrum and the wavelength-resolved photochemical reactivity has been observed. Herein, we unravel the underlying mechanisms behind the mismatched reactivity and absorbance in photocycloadditions. Initially, we probe the impact that an equilibrium established between reversible photochemical processes has on the mismatch for a pyrene-chalcone molecule. Subsequently, we establish a critical link between photophysics and photochemistry with a theory based on the selective excitation of specific microenvironments, leading to molecular transitions that allow for favorable wavelength-dependent reactivity. Time-resolved and steady-state fluorescence spectroscopy measurements confirm the presence of this selectivity, with both displaying significant red-edge effects that are observed in the fluorescence spectroscopy literature, further supporting our theory. By synthetically tethering chromophores together, we evidence the importance of microenvironments and their wavelength-dependent excited-state lifetimes, presenting the missing link that explains the mismatch in many photochemical systems. The implications of the theory presented herein stretch from additive manufacturing to photodynamic therapy and beyond, meaning that researchers can leverage mismatched photochemical reactivity by simply changing the properties of the environment surrounding the chromophore.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".