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Record W3091737399 · doi:10.1021/bk-2020-1358.ch010

Nucleation and Growth of Crystal on a Substrate Surface: Structure Matching at the Atomistic Level

2020· book-chapter· en· W3091737399 on OpenAlexaff
Xiancai Lu, Chi Zhang, Xiangjie Cui, Tingting Zhu, Meirong Zong

Bibliographic record

VenueACS symposium series · 2020
Typebook-chapter
Languageen
FieldEarth and Planetary Sciences
Topicnanoparticles nucleation surface interactions
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNucleationMaterials scienceMatching (statistics)CrystallographySubstrate (aquarium)Chemical physicsSurface (topology)Crystal growthCrystal (programming language)NanotechnologyGeometryChemistryComputer scienceThermodynamicsPhysicsMathematicsGeology

Abstract

fetched live from OpenAlex

In natural systems, surface-induced nucleation and growth of minerals on heterogeneous substrates are common on the Earth’s surface and deep subsurface environments. In the synthesis of crystalline materials, different templates have been successfully employed to prepare materials with a specific shape or orientation, or to speed up the synthesis. The structure match between the developed crystals and the substrates plays an important role in initial nucleation. This chapter first presents an overview of the heterogeneous nucleation of crystals on different substrates with varying degrees of mismatch, including highly matched structures, low mismatch with similar lattice parameters, and mismatched interface structures. Then an example about heterogeneous nucleation on substrates with highly matched structures is introduced. First-principles molecular dynamics (FPMD) simulation reveals the complexation of bivalent heavy-metal cations on edge surfaces of 2:1 clay mineral, and the kinetic processes of initial epitaxial growth are further described in detail.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.362
Threshold uncertainty score0.999

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)0.0020.000

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 head0.019
GPT teacher head0.203
Teacher spread0.185 · 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.

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

Citations3
Published2020
Admission routes1
Has abstractyes

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