ArticleJournal of fluorescence2025
Exploring New Azobenzene Type Photoswitches by Machine Learning with Lowest Possible Fluorescence Excitons with Ease of Synthesis.
Article in Journal of fluorescence, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- Enhanced Antimicrobial Photodynamic Effect Against Escherichia coli Using MXene-Based Silver-Curcumin 2D Nanocomposite Photosensitizers.Journal of fluorescence · 2026Article
- Predicting Stokes Shifts of Fluorescent Chromophores Using Ensemble Learning and H2OAutoML.Journal of fluorescence · 2025Article
- Synthesis and Antibacterial Studies of Silver Loaded TiOJournal of fluorescence · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Current investigation presents the design and analysis of 299 azobenzene photoswitches (PSs) for their lowest possible π→ π* transition energies along with their predicted emission maxima values through machine learning (ML) analysis. Their π→ π* transitions related wavelength is calculated by Erying equation to reveal its range up to 256 nm. Their Synthetic Accessibility Likelihood Index (SALI) indicates that a substantial number of them can be synthesized with ease. Among various tested ML model, eXtream Gradient Boosting (XGBoost) regression models demonstrates its high accuracy by achieving an R² value of 0.87. Their designed molecular descriptors show its Maximum Electrotopological State Index (MaxEStateIndex) to impact the model most. For its emission wavelengths, the random forest regression model yields its promising results with its R
Indexed as
Identifiers
40327276What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.