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Record W4410907921 · doi:10.18280/acsm.490204

Innovative Carbon Fiber-Reinforced Polypropylene for Enhanced Manufacturing of Lower-Limb Prosthetic Sockets

2025· article· en· W4410907921 on OpenAlexvenueno aff
Esraa A. Abbod, Shireen H. Challoob, Kadhim K. Resan, Ali Abed Salman, Mohammed Ali Abdulrehman, Ahmed K. Muhammad

Bibliographic record

VenueAnnales de Chimie Science des Matériaux · 2025
Typearticle
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsnot available
FundersMustansiriyah UniversityUniversiti Sains Malaysia
KeywordsPolypropyleneMaterials scienceComposite materialFiber

Abstract

fetched live from OpenAlex

Polypropylene prosthetic sockets are widely used in developing nations due to their low cost and ease of manufacture, encouraged by the ICRC.However, mechanical deterioration and creep reduce these sockets' performance.In this work, innovative polypropylene composites reinforced with chopped carbon fibers (5%-20%) are made using enhanced mixing and extrusion.The 15% carbon fiber composite proved superior to typical polypropylene in tensile testing, with an ultimate tensile strength of 67.3 MPa and a modulus of elasticity of 2451 MPa.Numerical analysis showed better safety and deformation resistance.The safety factor increased by 139%, from 1.9 to 4.55, demonstrating the reinforced socket's superior longevity and load-bearing capabilities.Finally, adding 15% chopped carbon fibers to polypropylene makes prosthetic sockets cost-effective and high-performance, making them excellent for resource-limited environments.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.021
GPT teacher head0.299
Teacher spread0.278 · 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

Citations2
Published2025
Admission routes1
Has abstractyes

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Same venueAnnales de Chimie Science des Matériaux→Same topicOrthopaedic implants and arthroplasty→French-language works237,207→