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Record W4379472048 · doi:10.5539/ijc.v15n2p1

Electrical Charge Method for Balancing, Quantifying, and Defining Redox Reactions

2023· article· en· W4379472048 on OpenAlexvenueno aff
Pong Kau Yuen, Cheng Man Diana Lau

Bibliographic record

VenueInternational Journal of Chemistry · 2023
Typearticle
Languageen
FieldChemistry
TopicVarious Chemistry Research Topics
Canadian institutionsnot available
Fundersnot available
KeywordsRedoxChemistryCharge (physics)ElectronFunction (biology)Chemical equationOxidation reductionChemical physicsBiological systemInorganic chemistryOrganic chemistryQuantum mechanicsPhysics

Abstract

fetched live from OpenAlex

Defining and balancing redox reactions are core knowledge and skills in the study of chemistry. The most common method to perform these two tasks is the oxidation number method, which combines mathematical operations and application of oxidation number. However, when oxidation number is not known, it is not applicable. Algebraic methods can balance all chemical reactions mathematically, but they cannot define redox reactions chemically. This article explores the electrical charge method for balancing, quantifying, and defining redox reactions. This method only requires the balancing of atoms and electrical charges. There is no need to determine oxidation number or count the number of transferred electrons. It works effectively in complicated cases where oxidation number is uncertain and where there are more than two sets of redox couples. Furthermore, the net-charge of a redox couple can function as a counting concept to determine its number of transferred electrons and change of oxidation numbers. The electrical charge method also initiates a new charge model, which complements the conventional electron model and oxidation number model, for defining redox reactions.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
Threshold uncertainty score0.593

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.041
GPT teacher head0.384
Teacher spread0.343 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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