MétaCan
Menu
Back to cohort
Record W2901128937 · doi:10.1007/s11666-019-00857-1

Beyond Traditional Coatings: A Review on Thermal-Sprayed Functional and Smart Coatings

2019· article· en· W2901128937 on OpenAlexaff

Bibliographic record

VenueJournal of Thermal Spray Technology · 2019
Typearticle
Languageen
FieldEngineering
TopicHigh-Temperature Coating Behaviors
Canadian institutionsUniversity of Alberta
FundersEngineering and Physical Sciences Research Council
KeywordsField (mathematics)CoatingMetal coating

Abstract

fetched live from OpenAlex

Thermal spraying has been present for over a century, being greatly refined and optimized during this time. It has become nowadays a reliable and cost-efficient method to deposit thick coatings with a wide variety of feedstock materials and substrates. Thermal-sprayed coatings have been successfully applied in fields such as aerospace or electricity production, becoming an essential component of today’s industry. To overpass the traditional capabilities of those coatings, new functionalities and coherent responses are being integrated, opening the field of functional and smart coatings. The aim of this paper is to present a comprehensive review of the current state of functional and smart coatings produced using thermal spraying deposition. It will first describe the different thermal spraying technologies, with a focus on how different techniques achieve the thermal and kinetic energy required to form a coating. It will as well focus on the environment to which feedstock particles are exposed in terms of temperature and velocity. It will first deal with the state-of-the-art functional and smart coatings applied using thermal spraying techniques; a discussion will follow on the fundamentals on which the coatings are designed and the efficiency of its performance; finally, the successful applications both current and potential will be described. The inherent designing flexibility of thermal-sprayed functional and smart coatings has been exploited to explore exciting new possibilities on many different fields. Some applications include, but not limited to, prevention of bacteria contamination and infection on hygienic environments. Here, thermal spray has been used to efficiently deposit antimicrobial compounds on medical furniture and appliances and to develop biocidal and biocompatible coatings for prosthetic implants. The attachment of hard and soft foulers such as algae or molluscs, which represents a considerable issue for any marine or freshwater installation, can be prevented on components where the use of traditional anti-fouling strategies such as paints is not optimal for certain materials (i.e., polymers). Another interesting approach pursued is the development of superhydrophobic surfaces, with contact angles as high as 160° and slide angles below 5°, leading to high droplet mobility. This adds capabilities as self-cleaning or corrosion resistance in addition to the characteristic robustness of thermal-sprayed coatings. The electric and magnetic properties of the feedstock materials have also led to the application of thermal spraying techniques in the creation of patterned structures with desired electromagnetic properties for their use on microelectronics. The possibility to intercalate layers of thermal-sprayed materials doped with optical-reactive elements has led to the development of online and offline temperature sensors which can be readily integrated in current thermal barrier coatings. To finalize the examples of the many applications of thermal-sprayed functional and smart coatings, autonomous self-healing or self-lubricant coatings have been developed. Advantage has been taken of a beneficial phase transformation triggered by the corresponding event (such as a crack or the tribological interactions, respectively) to promote self-healing. Another approach has been the release of an encapsulated component which effectively heals the coating or provides lubrication when required. All these exciting developments pave the way for the numerous applications that are to come in the next decade, making the field of thermal-sprayed coatings a unique opportunity for research and development.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.002

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 head0.012
GPT teacher head0.215
Teacher spread0.204 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations368
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Thermal Spray TechnologySame topicHigh-Temperature Coating BehaviorsFrench-language works237,207