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Record W7118498609 · doi:10.24294/jpse11906

Comprehensive case study and review of polymer–Cement waterproof coatings: Application of Dunlop K205 in chinese residential infrastructure

2025· article· W7118498609 on OpenAlexaboutno aff
Yuan Liu

Bibliographic record

VenueJournal of Polymer Science and Engineering · 2025
Typearticle
Language
FieldEngineering
TopicInnovative concrete reinforcement materials
Canadian institutionsnot available
Fundersnot available
KeywordsCoatingCementitiousCementContext (archaeology)PolymerAggregate (composite)

Abstract

fetched live from OpenAlex

Polymer–cement waterproof coatings couple the rigidity of cement and the elasticity of polymers into compositmembranes that prevent water penetration. In the system, polymer particles aggregate to form films in the cementitious matrix and hence plug up capillary pores and bridge micro- cracks].In this paper we present in depth case study of Dunlop K205—an acrylic polymer–cement coating for the Chinese market—and position the practical experience on the broader context of review of recent literature and commercial products. K205 was used to seal a high‑moisture bathroom in Santa Clara, USA. After six months, the coating was waterproof, flexible and did not delaminate even at elevated relative humidity.Compressive strengths and water absorptions are demonstrated in numerical results; polymer content is used to explain their variations in simulation. Comparison tables are provided among K205 and other products like Sika Damp Proofing Slurry, Drizoro Maxseal Flex and SinoMaco crystalline coatings. Permeability reduction formulas and bond strength formulas are derived for polymer contents. Real observations and simulation results for both of them are visualized by figures.We refer to 35articles — mostly which are published post-2020, according Vancouver style, wherein findings are reported and emphasized the benefits of polymer– cement waterproof coatings for residents as well as proper mixing, application and concerns related to sustainability issues.

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 categoriesMeta-epidemiology (narrow)
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 score0.059
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.005
GPT teacher head0.266
Teacher spread0.261 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

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