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Record W4409537726 · doi:10.1016/j.jmrt.2025.04.162

Interfacial properties determine the performance of embedded IrOx pH sensor in cement mortar

2025· article· en· W4409537726 on OpenAlexfundno aff
Yuanxia Wang, Tianyu Li, Nazhen Liu, Quantong Jiang, Xiangju Liu, Hui-Wen Tian, Congtao Sun, Lijun Song, Chengtao Li, Baorong Hou

Bibliographic record

VenueJournal of Materials Research and Technology · 2025
Typearticle
Languageen
FieldChemical Engineering
TopicAnalytical Chemistry and Sensors
Canadian institutionsnot available
FundersNational Natural Science Foundation of ChinaCanadian Anesthesiologists' Society
KeywordsMaterials scienceCementMortarComposite materialChemical engineering

Abstract

fetched live from OpenAlex

IrO x electrode has been reported to show promising application in pH determination in many environments. Once embedded in cement mortar, the performance of IrO x electrode is poor. The failed pH determination is due to inaccurate potential measurement of embedded IrO x electrodes, including potential difference across aqueous solution/mortar interface ( E 1 ), potential difference generated due to transport of charge carriers in pore structure of cement mortar ( E 2 ), and potential difference across interfacial transition zone (ITZ)/IrO x electrode interface ( E 3 ). E 1 is measured to be 19.6 mV, leading to a lower calculated pH by 0.39. E 2 is 20.9 mV, indicating E 2 associated error in pH determination is also minor (0.42 pH). E 3 (of −317.3 mV) is confirmed to introduce serious errors in potential measurement, due to excessive resistance across the ITZ area. The microstructure of ITZ is studied by X-ray computed tomography (μXCT). In the case of OPC, abnormal porosity is observed. Although the continuous pores can't be detected by μXCT, it is proposed the presence of abundant continuous pores is essential for a meaningful measurement of the IrO x electrode potential and the subsequent calculation of pH in pore water.

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 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 score0.002
Threshold uncertainty score0.170

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)0.0000.000

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 head0.036
GPT teacher head0.307
Teacher spread0.271 · 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
Published2025
Admission routes1
Has abstractyes

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