Evidence map›Paper›PMID 32923774›Full record

ArticleACS omega2020

Softened Wood Treated by Deep Eutectic Solvents.

Yan Wu, Lechen Yang, Jichun Zhou, Feng Yang, Qiongtao Huang, Yijing Cai

Abstract read
In one paragraph

Article in ACS omega, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Yan WuCollege of Furnishings and Industrial Design, Nanjing Forestry University, Nanjing 210037, China.
Lechen YangCollege of Furnishings and Industrial Design, Nanjing Forestry University, Nanjing 210037, China.
Jichun ZhouCollege of Furnishings and Industrial Design, Nanjing Forestry University, Nanjing 210037, China.
Feng YangFashion Accessory Art and Engineering College, Beijing Institute of Fashion Technology, Beijing 100029, China.
Qiongtao HuangDepartment of Research and Development Center, Yihua Lifestyle Technology Co., Ltd., Shantou 515834, China.
Yijing CaiCollege of Furnishings and Industrial Design, Nanjing Forestry University, Nanjing 210037, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Due to its good physical properties, softened wood (SW) has been widely used in the fields of home furnishing, interior decoration, and construction, such as decorative panels, softened wood flooring, wooden bricks, and softened wood furniture. However, traditional methods of wood softening often fail to meet the requirements of enterprises for softening wood. Here, inspired by the research related to wood softening, we propose a method for directly preparing softened wood (SW) using a new type of "ionic liquid" eutectic solvent (DES) owing to its low cost, environmental friendliness, recyclability, and other advantages. To improve the adaptability of the study, a total of five types of DESs were designed and prepared, and by the microwave-assisted DES treatment of natural wood (NW), the purpose of softening wood was achieved. Then, we conducted a series of comparative analyses and performance tests on NW and SW, including microscopic images, chemical composition, color difference, and mechanical properties. The results show that the wood softened by DES has become a highly porous network structure, and partial lignin, hemicellulose, and cellulose have been removed. At the same time, different degrees of color change, lower hardness, excellent mechanical flexibility, and a compression rebound rate of up to about 90% are obtained. The above-mentioned various properties of SW provide great potential for its application in wood products.

Identifiers

PMID32923774
PMCPMC7482086

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.