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Record W4247821819 · doi:10.1201/b11485-15

On the Negative Poisson’s Ratios and Thermal Expansion in Natrolite

2012· book-chapter· en· W4247821819 on OpenAlexfundno aff

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldMaterials Science
TopicFerroelectric and Piezoelectric Materials
Canadian institutionsnot available
FundersNational Research Council Canada
KeywordsPoisson distributionThermodynamicsChemistryPhysicsMathematicsStatistics

Abstract

fetched live from OpenAlex

Throughout history, civilizations have been distinguished according to their ability to produce and work with superior materials that perform better than the previously available ones. In fact, the different eras in our prehistory are dežned by the materials available to our ancestors: stone, bronze, and then iron, where clearly the newer materials proved to be far better than the previous ones in terms of mechanical strength, durability, and workability. The search for new and superior materials has always been a crucial matter in the advancements of humanity and is still in fact an ongoing process. Since the beginning of the twentieth century, research in materials science resulted in massive developments that made possible the big advances in the communications and transport industries, among others. In particular, the last three decades have seen an increased interest in designing, discovering, and synthesizing 10.1 Introduction .................................................................................................. 135 10.1.1 Materials Exhibiting Negative Poisson’s Ratios (Auxetic) ............... 136 10.1.2 Negative Thermal Expansion ........................................................... 138 10.1.3 Causes of Negative Poisson’s Ratios and Negative Thermal Expansion ......................................................................................... 139 10.2 Modeling and Experimental Work on the Thermo-Mechanical Properties of Natrolite .................................................................................. 140 10.2.1 Mechanical Behavior of NAT-Type Systems .................................... 140 10.2.2 Thermal Behavior of NAT-Type Systems ......................................... 144 10.3 Results and Discussion ................................................................................. 147 10.4 Conclusion .................................................................................................... 149 Acknowledgments .................................................................................................. 149 References .............................................................................................................. 149 new materials that exhibit highly unusual properties. For example, although one normally expects that a lateral contraction should occur when a material is uniaxially stretched, it has been shown that not all materials behave like this and some materials can actually get fatter when uniaxially stretched (auxetic) (Lakes 1987; Evans et al. 1991; Wojciechowski 1989). Similarly, although one normally expects a material to expand when heated, some materials actually shrink in size, that is, they undergo negative thermal expansion (NTE). As discussed below, such properties usually arise as a result of the manner in which particular features in the nano-or microstructure of the material deform upon stretching or heating and result in the materials having superior qualities when compared with their conventional counterparts.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.219
Teacher spread0.202 · 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 source (direct Gemma or distilled Codex), 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

Citations5
Published2012
Admission routes1
Has abstractno

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