The Structure and Decay Dynamics of Exciplexes Derived from Dibenzoylmethanatoboron Difluoride and Alkylbenzenes in Cyclohexane
Bibliographic record
Abstract
Dibenzomethanatoboron difluoride (DBMBF2) interacted with alkylbenzenes from its singlet excited state to form exciplexes ranging from weak polarity up to contact radical ion pairs (CRIP); this exciplex series shows the characteristics in the Marcus "normal" region. In cyclohexane these exciplexes gave intense fluorescence spectra and high quantum yields (phi(infinity)ex). The dipole moment of these exciplexes calculated from the solvatochromic shift of the fluorescence maximums (vmax) was used to estimate the coefficient ("a" and "c") of the CT and LE terms in the exciplex wavefunction. On the basis of the measured lifetimes and phi(infinity)ex of these exciplexes, the radiative (k(ex)f) and nonradiative (k(ex)NR) rate constants were calculated. The former k(ex)f were also computed from a semi-empirical approach based on the assumption that the exciplex wavefunction could be adequately described by CT and LE states, and that *DBMBF2 primarily contributes to the probability of exciplex emission. Two results agree with each other with small systematic deviations for those less polar exciplexes. The plots of k(ex)r and k(ex)NR (or their logarithmic value) against the LE contribution (c2) and transition energy gaps (hvmax) afford better correlation than those against -deltaG(-et). This indicates the role played by the LE contribution in generating the stabilization energy (U(s)) in these exciplexes through the A-D+)<==> *AD) resonance interaction; U(s), in turn, modifies -deltaG(-et) to afford the decay driving force hvmax. Also, those plots from k(ex)f values (being determined directly from experiments) show better correlation than those from k(ex)NR. In contrast to the CRIP type exciplexes in the Marcus "inverted" region, these k(ex)f and k(ex)NR increase in the common trend with increasing transition energy gaps. The k(ex)NR plots show less steep slopes and attains more quickly a minimum toward the CRIP region; the latter is identified as the turning point from the "normal" to "inverted" region. Both the attenuation and reversal of the k(ex)NR value with increasing polarity are believed to be generated by the emerging contribution of the intersystem crossing process as an additional nonradiative process, which is induced by the increased spin-orbit coupling in highly polar exciplexes.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".