Automated monitoring the kinetics of homogeneous and heterogeneous chemical processes using a smartphone
Bibliographic record
Abstract
Abstract Heterogeneous chemical processes occupy a pivotal position in many fields of applied chemistry. Monitoring reaction kinetics in such heterogeneous systems together with challenges associated with ex-situ analytical methodologies can lead to inaccurate information about the nature of the catalyst surfaces as well as information about the steps involved. The present work explores the possibility of kinetic measurements of chemical reactions and adsorption processes of homogeneous and heterogeneous systems through the variation of RGB intensities of digital images using a smartphone combined with a program written in Python to accelerate and facilitate data acquisition. In order to validate the method proposed, the base promoted hydrolysis of 4-nitrophenyl acetate was initially investigated. The rate constants obtained through RGB analysis (0.01854 min −1 ) is almost identical to that using traditional UV–Vis spectroscopy (0.01848 min −1 ). The proposed method was then applied to monitor the kinetics of three heterogeneous processes: (1) reduction of 4-nitrophenolate in the presence of dispersed Pd/C; (2) decomposition of methyl orange with TiO 2 ; and (3) adsorption of rhodamine on montmorillonite. In general, the method via digital images showed high reproducibility and analytical frequency, allowing the execution of simultaneous analyses, with an accuracy comparable to UV–Vis spectrophotometry. The method developed herein is a practical and valuable alternative for obtaining kinetic data of heterogeneous reactions and processes where a color change is involved, bypassing sampling collection and processing which decreases analytical frequency and may lead to data errors.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".