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Record W4387372893 · doi:10.1016/j.jmrt.2023.10.028

Structure and mechanisms of foam-like bamboo parenchyma tissue

2023· article· en· W4387372893 on OpenAlexaff
Qin Su, Lin Chen, Chunping Dai, Xiaohan Chen, Xun Luo, Changhua Fang, Xinxin Ma, Xiubiao Zhang, Huanrong Liu

Bibliographic record

VenueJournal of Materials Research and Technology · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBamboo properties and applications
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMaterials sciencePorosityBambooComposite materialParenchymaPorous medium

Abstract

fetched live from OpenAlex

The increased awareness of environmental problems has shifted the attention from synthetic polymer porous materials to natural plant porous materials like bamboo parenchyma tissue to develop functional materials. However, the neglect of the inherent structure and property of plant porous materials leads to insufficient development and utilization of their natural advantages. Here, we investigated the fine structure and mechanisms of bamboo parenchyma tissue, which has multiple high-quality properties like energy-absorbing and toughening, to provide a basis for their wide utilization in porous materials. Results showed that bamboo parenchyma tissue was a foam-like porous material with sufficient strength, high porosity and low density. It was composed of cell walls and four types of three-dimensional polyhedral pores converging in space. The total natural porosity was up to 78.80%. The cell walls exhibited a three-zone strength distribution with an average solid cell wall strength of 75.84 MPa, due to their cocoon-like closed microfibrils patterns, chemical compositions and pits characteristics. The high porosity and weakening of transition zone and cell end wall helped to absorb energy. This study provides ideas for the application of bamboo parenchyma tissue in cushioning energy-absorbing foam materials and porous material templates and for the biomimetic design of environmentally friendly porous materials.

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)0.000
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 score0.125
Threshold uncertainty score0.178

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.047
GPT teacher head0.302
Teacher spread0.255 · 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

Citations15
Published2023
Admission routes1
Has abstractyes

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