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Growth-promoting Effect of Graphene Oxide on Tissue Culture Seedlings of Poplar

2024· preprint· en· W4391402871 on OpenAlexaff
Runxuan Zhang, Baoyan Xing, Ya‐Yan Bao, Haiyan Liu, Jingting Huang, Bin-long Xue, Jianfeng Li, Haiyan Wang, Jianzhong Yao, Xinjun Zhou, Christopher Oberc, Paul Li

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicGraphene and Nanomaterials Applications
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsPlasmolysisGrapheneOxidePoint of deliveryChemistryStarchCell wallBotanyHorticultureBiophysicsFood scienceBiologyBiochemistryMaterials scienceNanotechnologyOrganic chemistry

Abstract

fetched live from OpenAlex

Graphene oxide (GO) is a widely used nanomaterial, whose unique carbon-based structure bears abundant oxygen-containing functional groups. An increasing number of investigations have been performed on the effects of GO on plants due to its extensive use in various fields. In this work, under the optimized GO concentration (6 mg/L), we found that GO promoted the growth rate of the root of poplar. TEM images depicted that the plasmolysis and an increased number of starch grains were the two major physiological changes in the root cell samples. GO adhered to the cell membrane on the inner side of the cell wall and enhanced the toughness of the adventitious root. And the activity of oxidoreductases were measured and SOD, POD were found to increase significantly compared with the control group, but the increase of CAT was not obvious. Related gene expression was enhanced at low concentration of GO, but inhibited at high concentration. Our results indicate the possible usage of GO as enhancers in the growth of poplar tissue culture seedlings.

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 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 score0.007
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.004
GPT teacher head0.222
Teacher spread0.218 · 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
Published2024
Admission routes1
Has abstractyes

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