Chemical Reaction Stoichiometry: A Key Link between Thermodynamics and Kinetics, and an Excel Implementation
Bibliographic record
Abstract
The Law of Conservation of Mass (LCM) is one of the most important principles in chemistry. It applies to both closed and steady-state open flow systems undergoing chemical change. Various special methods are generally taught for its implementation, including inspection, oxidation-reduction, and ion-electron approaches, which typically fall under the topic of "balancing a chemical equation". However, apart from the simplest case described by a single such equation, only matrix methods are applicable. This paper describes Chemical Reaction Stoichiometry (CRS), and its implementation of the LCM for chemically reacting systems by its expression in terms of a nonunique set of independent chemical equations of the appropriate number. Such equations have the superficial appearance of, but are distinct from, an actual chemical reaction mechanism. The underlying matrix method is based on ideas from basic linear algebra; in addition to being generally applicable to systems of any complexity, it obviates the need for the aforementioned special methods in single-reaction systems. We provide an easy-to-use spreadsheet implementation of CRS that includes many worked examples.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.067 | 0.043 |
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".