Experimental methods in chemical engineering: Micro‐reactors
Bibliographic record
Abstract
Whereas the bulk chemical industry has historically sought economic advantage through economies of scale, a paradigm shift has researchers developing systems on smaller scales. Nano‐cages and nano‐actuators increase selectivity and robustness at the molecular scale. In parallel, micro‐contactors with sub‐millimetre lateral dimensions are decreasing boundary layers that restrict heat and mass transfer and thus meet the objectives of process intensification with great increases in productivity with a smaller footprint. These contactors continue to serve chemical engineers and chemists to synthesize fine chemicals and characterize catalysts; however, they have now been adopted for sensors in biological and biochemical systems. A bibliometric analysis of articles indexed in the Web of Science in 2016 and 2017 identified five major clusters of research: catalysis and bulk chemicals; nanoparticles; organic synthesis and flow chemistry; systems and micro‐fluidics applied to biochemistry; and micro‐channel reactors and mass transfer. In the early 1990s, less than 100 articles a year mentioned micro‐reactors, while over 943 articles mentioned it in 2017. Here, we introduce micro‐reactors and their role in the continuous synthesis of fine chemicals across the various scales to commercialization.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.016 |
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".