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Record W4321498643 · doi:10.3390/materproc2023013039

Thermal Performance Evaluation of a PCM-Integrated Gypsum Plaster Board

2023· article· en· W4321498643 on OpenAlexaff
Katarzyna Nowak, Tomasz Kisilewicz, Umberto Berardi, Anna Zastawna-Rumin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPhase Change Materials Research
Canadian institutionsToronto Metropolitan University
FundersNarodowa Agencja Wymiany Akademickiej
KeywordsPhase-change materialMaterials scienceHeat capacityLatent heatHysteresisGypsumThermalEnthalpyProcess engineeringHeat flowFlow (mathematics)Mechanical engineeringComposite numberPhase changeComputer scienceComposite materialThermodynamicsMechanicsEngineering

Abstract

fetched live from OpenAlex

In order to design and optimize building materials containing phase changing material (PCM), it is important to accurately characterize the thermal properties of these composites: the enthalpy curve and its hysteresis. This paper presents the initial tests of these properties for a composite panel: gypsum plasterboard with an evenly distributed layer of PCM. Performance testing of the selected material was carried out by means of the dynamic method. This method (dynamic heat flow meter apparatus (DHFMA)) involves the measurement of non-stationary heat flow to determine the stored energy (enthalpy change) as a function of temperature using a stationary heat flow measurement apparatus (HFMA). This method allows for the measurement of the sensible and latent heat capacity of the products containing phase change material. In addition to presenting the obtained results, the study will discuss the practical aspects of this test method, recently introduced in the standard ASTM C 1784-20. The preliminary experiments described in the article were aimed at learning a new research technique, determining the required conditions for conducting research and the capabilities of the possessed apparatus in this regard.

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)0.000
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 score0.577
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0020.001

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 head0.093
GPT teacher head0.315
Teacher spread0.222 · 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

Citations8
Published2023
Admission routes1
Has abstractyes

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