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Record W4300198397

Clay/Polyethylene nanocomposites with enhanced barrier properties for long-term seed storage

2016· preprint· en· W4300198397 on OpenAlexaff
Luyang Hu, Elisabeth Leclair, M. Poulin, Fabienne Colas, Patrick Baldet, Pascal Y. Vuillaume

Bibliographic record

VenueHAL (Le Centre pour la Communication Scientifique Directe) · 2016
Typepreprint
Languageen
FieldMaterials Science
TopicPolymer Nanocomposites and Properties
Canadian institutionsMinistère des Ressources naturelles et des ForêtsCegep de Thetford
Fundersnot available
KeywordsPolyethyleneMaterials scienceNanocompositeTerm (time)Composite materialPhysics
DOInot available

Abstract

fetched live from OpenAlex

There are numerous threats to the biodiversity of commercial and non-commercial plant species. The easiest way to prevent biodiversity loss is to conserve their genetic resources in seed banks.\nDepending on seeds sensitivity, it is vital to provide seeds packaging protection with high water vapor barrier components. Currently, most of container materials for seed banking are made from glass or tri-laminated foil. Glass containers are heavy and easily broken during handling and transport, whereas tri-laminated foil pouches are opaque and subjected to puncture by sharp seeds. High density polyethylene (HDPE) containers are rarely used for long term seed storage due to their permeability to moisture vapor. Considering the numerous advantages of HDPE such as low cost, lightweight, chemical inertness and easy processing, HDPE with enhanced barrier properties is suitable for long-term seed storage.\nAccording to previous research work, the addition of nanoclays into polymer matrix leads to the tortuous path that delayed the diffusion of gaseous molecules as water vapour. \nThe present study aims to evaluate the effect of different clays on the overall properties of HDPE, especially water vapor barrier properties. \nThe water vapor barrier of tested samples was found to be influenced by several factors: the aspect ratio of clays, the crystallinity of HDPE, the interface between clays and HDPE and the storage conditions. / Il y a des nombreuses menaces qui pèsent sur la biodiversité des espèces végétales commerciales et non commerciales. La meilleure façon de prévenir la perte de biodiversité est de conserver leurs ressources génétiques dans des banques de semences.\nSelon la sensibilité des graines, il est essentiel d'utiliser des contenants composés de matériaux faisant hautement barrage à la vapeur d'eau. Actuellement, la plupart des conteneurs pour les banques de semences est en verre ou en complexe laminé aluminium /plastique. Les récipients en verre sont lourds et fragiles pendant les manutentions et le transport, alors que les sachets laminés aluminium/plastique sont opaques et sensibles à la perforation par des graines acérées. Les contenants en polyéthylène haute densité (PEHD) sont rarement utilisés pour le stockage long terme de semences en raison de leur perméabilité à la vapeur d'eau. Étant donné les nombreux avantages du PEHD tels que l'inertie chimique, la légèreté, leur faible coût ainsi que leur mise en oeuvre facile, le PEHD doté de propriétés de barrière renforcées est adapté au stockage à long terme des semences.\nComme démontré dans les travaux de recherche précédents, l'ajout de nano-argiles dans la matrice polymère conduit à la mise en place de chemins tortueux qui retardent la diffusion des molécules gazeuses comme la vapeur d'eau. \nLa présente étude vise à évaluer l'effet des différentes argiles sur les propriétés globales de HDPE et en particulier les propriétés de barrière de vapeur d'eau. \nLa capacité barrière de vapeur d'eau des échantillons testés s'est avérée être influencée par plusieurs facteurs : l'allongement des argiles, la cristallinité du PEHD, l'interface entre les argiles et le PEHD ainsi que les conditions de stockage.

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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.014
GPT teacher head0.220
Teacher spread0.206 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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