Evidence map›Paper›PMID 38304106›Full record

ArticleFrontiers in bioengineering and biotechnology2023

Pretreatment and enzymatic hydrolysis optimization of lignocellulosic biomass for ethanol, xylitol, and phenylacetylcarbinol co-production using

Kritsadaporn Porninta, Julaluk Khemacheewakul, Charin Techapun, Yuthana Phimolsiripol, Kittisak Jantanasakulwong, Sumeth Sommanee, Chatchadaporn Mahakuntha, Juan Feng, Su Lwin Htike, Churairat Moukamnerd and 8 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in bioengineering and biotechnology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
2.6field-weighted citation impact, top 11% of its field
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

6 citing papers in PubMed, 14 citations in OpenAlex.

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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

18 authors at 4 institutions in 3 countries.

Kritsadaporn PornintaProgram in Biotechnology, Multidisciplinary and Interdisciplinary School, Chiang Mai University, Chiang Mai, Thailand.
Julaluk KhemacheewakulCluster of Agro Bio-Circular-Green Industry (Agro BCG), School of Agro-Industry, Faculty of Agro-Industry, Chiang Mai University, Chiang Mai, Thailand.
Charin TechapunCluster of Agro Bio-Circular-Green Industry (Agro BCG), School of Agro-Industry, Faculty of Agro-Industry, Chiang Mai University, Chiang Mai, Thailand.
Yuthana PhimolsiripolCluster of Agro Bio-Circular-Green Industry (Agro BCG), School of Agro-Industry, Faculty of Agro-Industry, Chiang Mai University, Chiang Mai, Thailand.
Kittisak JantanasakulwongCluster of Agro Bio-Circular-Green Industry (Agro BCG), School of Agro-Industry, Faculty of Agro-Industry, Chiang Mai University, Chiang Mai, Thailand.
Sumeth SommaneeCluster of Agro Bio-Circular-Green Industry (Agro BCG), School of Agro-Industry, Faculty of Agro-Industry, Chiang Mai University, Chiang Mai, Thailand.
Chatchadaporn MahakunthaCluster of Agro Bio-Circular-Green Industry (Agro BCG), School of Agro-Industry, Faculty of Agro-Industry, Chiang Mai University, Chiang Mai, Thailand.
Juan FengCluster of Agro Bio-Circular-Green Industry (Agro BCG), School of Agro-Industry, Faculty of Agro-Industry, Chiang Mai University, Chiang Mai, Thailand.
Su Lwin HtikeCluster of Agro Bio-Circular-Green Industry (Agro BCG), School of Agro-Industry, Faculty of Agro-Industry, Chiang Mai University, Chiang Mai, Thailand.
Churairat MoukamnerdFaculty of Agro-Industry, Chiang Mai University, Chiang Mai, Thailand.
Xinshu ZhuangGuangzhou Institute of Energy Conversion, Chinese Academy of Sciences, CAS Key Laboratory of Renewable Energy, Guangdong Provincial Key Laboratory of New and Renewable Energy Research and Development, Guangzhou, China.
Wen WangGuangzhou Institute of Energy Conversion, Chinese Academy of Sciences, CAS Key Laboratory of Renewable Energy, Guangdong Provincial Key Laboratory of New and Renewable Energy Research and Development, Guangzhou, China.
Wei QiGuangzhou Institute of Energy Conversion, Chinese Academy of Sciences, CAS Key Laboratory of Renewable Energy, Guangdong Provincial Key Laboratory of New and Renewable Energy Research and Development, Guangzhou, China.
Fu-Li LiKey Laboratory of Biofuels, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Shandong Energy Institute, Qingdao New Energy Shandong Laboratory, Qingdao, China.
Tianzhong LiuKey Laboratory of Biofuels, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Shandong Energy Institute, Qingdao New Energy Shandong Laboratory, Qingdao, China.
Anbarasu KumarCluster of Agro Bio-Circular-Green Industry (Agro BCG), School of Agro-Industry, Faculty of Agro-Industry, Chiang Mai University, Chiang Mai, Thailand.
Rojarej NuntaCluster of Agro Bio-Circular-Green Industry (Agro BCG), School of Agro-Industry, Faculty of Agro-Industry, Chiang Mai University, Chiang Mai, Thailand.
Noppol LeksawasdiCluster of Agro Bio-Circular-Green Industry (Agro BCG), School of Agro-Industry, Faculty of Agro-Industry, Chiang Mai University, Chiang Mai, Thailand.
Chiang Mai University · THGuangzhou Institute of Energy Conversion · CNQingdao Institute of Bioenergy and Bioprocess Technology · CNPeriyar Maniammai Institute of Science & Technology · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cellulosic bioethanol production generally has a higher operating cost due to relatively expensive pretreatment strategies and low efficiency of enzymatic hydrolysis. The production of other high-value chemicals such as xylitol and phenylacetylcarbinol (PAC) is, thus, necessary to offset the cost and promote economic viability. The optimal conditions of diluted sulfuric acid pretreatment under boiling water at 95°C and subsequent enzymatic hydrolysis steps for sugarcane bagasse (SCB), rice straw (RS), and corn cob (CC) were optimized using the response surface methodology via a central composite design to simplify the process on the large-scale production. The optimal pretreatment conditions (diluted sulfuric acid concentration (% w/v), treatment time (min)) for SCB (3.36, 113), RS (3.77, 109), and CC (3.89, 112) and the optimal enzymatic hydrolysis conditions (pretreated solid concentration (% w/v), hydrolysis time (h)) for SCB (12.1, 93), RS (10.9, 61), and CC (12.0, 90) were achieved. CC xylose-rich and CC glucose-rich hydrolysates obtained from the respective optimal condition of pretreatment and enzymatic hydrolysis steps were used for xylitol and ethanol production. The statistically significant highest (

Indexed as

ethanoloptimizationphenylacetylcarbinolresponse surface methodologysustainabilityxylitol

Identifiers

PMID38304106
PMCPMC10830760
OpenAlexW4390980493

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.