Bibliographic record
Abstract
Abstract Chemical engineering research encompasses a plethora of subjects ranging from nanoscale to climate change, from medicine to hazardous waste management, and from electronics and photonics to process intensification, manufacturing, and modelling. The 2017 AIChE meeting included 8000 oral presentations and posters with all these diverse topics. Here we identify experimental methods and instrumentation that straddle these subjects based on Can. J. Chem. Eng . articles published from 2016–2017. Temperature, pH, pressure, and flow rate are the physical properties most researchers report. We identified over 50 experimental techniques, reactor types, and modelling methodologies and show that articles with an experimental focus are more strongly linked than those that concentrate on hydrodynamic modelling and numerical simulation. Spectroscopy dominates instrumental techniques to characterize solids and catalyst properties and we classify 36 instruments according to what property they measure and the scale, nature of the phase, composition, and morphology. A bibliometric analysis grouped the physicochemical properties into three major research clusters centered around solid properties, liquid phase properties, and gas phase and aqueous phase properties. Articles in this special series describe the basic principles of one experimental method or instrument that appears frequently in the journal. Together with a short background, the articles highlight fields of application, assess the sources of error, detection thresholds, and uncertainty. This series provides chemical engineers with a concise, accessible reference guide to help identify and choose appropriate experimental techniques and instruments.
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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 | 0.001 |
| 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) | 0.000 | 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".