INNOVATION IN GREEN PROCESS ENGINEERING UNDERGRADUATE LABORATORY COURSE - INTEGRATED LABORATORIES FOR PARTICULATE OPERATIONS, HEAT TRANSFER AND MASS TRANSFER COURSES
Bibliographic record
Abstract
In the past two years since 2011, the course instructor (Dr. Xu), along with the students in the Green Process Engineering (GPE) class and TAs, has developed an innovative undergraduate laboratory course that integrates laboratories for particulate operations, heat and mass transfer courses. The integrated lab course runs as research projects that apply and integrate the concepts reviewed in the above courses. One of the key objectives of this course is to train team work and leadership. To this end, the students are grouped into 4 groups, and each group carries out one of the following 4 projects for 6h/week and approx.6 weeks, rotates the projects and completes all by the end of this full-year course: (1) Particulate operations - heterogeneous catalyst particles (Au/MgAl2O4) formation, handling and characterization; (2) Convective heat transfer enhancement in a stirred tank reactor; (3) Liquid phase mass transfer in a gas-liquid stirred reactor system; (4) A green process for the production of acetic acid via aqueous phase oxidation of ethanol with air using Au/MgAl2O4 catalyst: effects of mass transfer and reaction kinetics. As the course learning objectives, students should be able to propose experimental methodologies and design their own experimental procedure, secure and prepare their own experimental materials and equipment and facilities, perform the experiments and collect data, interpret the experimental results using the principles and knowledge from the relevant courses, and present their results effectively.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.041 | 0.019 |
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 source (direct Gemma or distilled Codex), 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".