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Record W4317543081 · doi:10.1021/acs.jchemed.2c00777

The Importance of Synthesis Conditions: Structure–Processing–Property Relationships

2023· article· en· W4317543081 on OpenAlexafffund
Shuang Qiu, Dongyang Zhang, Vishal Yeddu, Cristina Cordoba, Arthur M. Blackburn, Violeta Iosub, Makhsud I. Saidaminov

Bibliographic record

VenueJournal of Chemical Education · 2023
Typearticle
Languageen
FieldEngineering
TopicPerovskite Materials and Applications
Canadian institutionsUniversity of Victoria
FundersBritish Columbia Knowledge Development FundNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCanada Foundation for Innovation
KeywordsCrystalliteCrystallizationProperty (philosophy)Crystal structureMaterials scienceCrystal (programming language)Chemical physicsPerovskite (structure)NanotechnologyChemistryCrystallographyComputer scienceOrganic chemistry

Abstract

fetched live from OpenAlex

When structure–property relationships are discussed in inorganic chemistry and materials science, the common perception is that materials with the same composition and atomic arrangement have the same properties. In this laboratory experiment, we show that processing ─the exact conditions under which materials are made─can in fact significantly alter the properties of materials. Using halide perovskite as a model, we demonstrate that the same set of starting chemicals, when processed differently, can form distinct material platforms–single crystals, thin films, and nanoparticles, the last of these being made of crystallites that are 10,000 times smaller than the first. We then show how this difference in crystal size leads to major changes in the optical properties of the materials, despite the fact that they have the same composition and crystal structure. The experiment provides an example of how the basic concept of crystallization leads to different materials and how processing affects the crystal size and thus influences the structure–property relationship.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.071
Threshold uncertainty score0.126

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.017
GPT teacher head0.263
Teacher spread0.246 · 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

Citations6
Published2023
Admission routes2
Has abstractyes

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