ArticleCommunications chemistry2025
Real-time inline-IR-analysis via linear-combination strategy and machineś learning for automated reaction optimization.
Article in Communications chemistry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Phase-aware adaptation for functional group recognition from condensed-phase infrared spectra.RSC advances · 2026Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Automation has revolutionized many fields by improving efficiency, accuracy, and reproducibility. However, in organic chemistry, automating key tasks such as reaction optimization and analysis remains a significant challenge. To accelerate advancements in organic chemistry research and development, we propose a fully automated system based on real-time inline analysis performed by Fourier-transform infrared spectroscopy and assisted by a neural network model. To rapidly collect data, a linear combination of spectral intensities was used as training data for a yield prediction model. Using this model, we demonstrated real-time yield prediction of Suzuki-Miyaura cross-coupling with remarkable accuracy. By combining this yield prediction model with real-time inline analysis and a flow chemistry setup, we have developed a fully automated system for the rapid and efficient optimization of reaction conditions and process analysis.
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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 OpenQuestion graph.