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Record W2594579296

Catalytic effect of MgCl2 on cellobiose decomposition in hot-compressed water

2015· article· en· W2594579296 on OpenAlexaboutno aff
Yun Yu, Zainun Mohd Shafie, Hongwei Wu

Bibliographic record

VenueAsia Pacific Confederation of Chemical Engineering Congress 2015: APCChE 2015, incorporating CHEMECA 2015 · 2015
Typearticle
Languageen
FieldEngineering
TopicCatalysis for Biomass Conversion
Canadian institutionsnot available
Fundersnot available
KeywordsCellobioseChemistryHydrothermal circulationDecompositionLignocellulosic biomassCelluloseCatalysisOrganic chemistryFructoseChemical engineeringCellulase
DOInot available

Abstract

fetched live from OpenAlex

There is an increasing interest in producing renewable biofuels and platform chemicals from hydrothermal processing of biomass-derived sugars in hot-compressed water (HCW). Sugar monomers (i.e., glucose and fructose) are known to be good feedstock for producing platform chemicals such as 5-hydroxymethylfurfural (5-HMF) via catalytic hydrothermal processing. Unfortunately, hydrothermal depolymerisation of biomass and cellulose mainly produces sugar oligomers with various degrees of polymerization as primary products [5, 6]. Hydrothermal decomposition of these sugar oligomers are poorly understood. Cellobiose as a dimer was used as a model compound to investigate the hydrothermal decomposition of sugar oligomers in recent studies. Under non-catalytic conditions, cellobiose hydrothermal decomposition mainly proceeds via isomerization reactions to produce cellobiulose (glucosyl-fructose, GF) and glucosyl-mannose (GM), while the contribution of hydrolysis reaction is small. As biomass contains abundant alkali and alkaline earth metallic (AAEM) species, and these species are soluble in high temperature water, these water-soluble AAEM species has a large influence on biomass hydrothermal conversion. Mg2+ and Ca2+ can act as Lewis acids to catalyse cellobiose hydrothermal decomposition by promoting isomerization reactions. However, the underlying catalytic mechanism is still unclear. This extended abstract further reports a mechanistic investigation into the roles of MgCl2 on the decomposition mechanisms of cellobiose at 200-250 degreesC and a pressure of 10 MPa. A continuous reactor system was employed, similar to that used in the previous studies. A series of cellobiose solutions (2.9 mM) with various MgCl2 concentrations of 8.7-87 mM (equivalent to a salt-tocellobiose molar ratio of 1-10) were prepared for experiments. The residence time of reactant solution was adjusted by the length of the stainless tube reactor and the flow rate of mixed stream. The effluent was cooled to room temperature in an ice water bath. The liquid products were analysed by a higher performance anion exchange chromatography (Dionex ICS-5000 with CarboPac PA20 analytic and guard columns) with pulsed amperometric detection and mass spectrometry (HPAEC-PAD-MS), using various standards purchased from Sigma-Aldrich and LC Scientific Inc (Canada). The detailed procedure for the HPAEC-PAD-MS analysis can be found elsewhere [14]. Total carbon contents of selected samples were also determined by a total organic carbon (TOC) analyser (Shimadzu TOCVCPH). It has been confirmed that the gas production from cellobiose decomposition is negligible, given that a carbon balance of 100% was achieved even at high temperatures. The cellobiose conversion was found to increase (buy not linearly) with an increase in MgCl2 concentration. The initial increase is large from 0 to 14.5 mM of MgCl2 concentration (equivalent to a salt-to-cellobiose molar ratio of 5) but further increase is limited when the MgCl2 concentration increases to 29 mM (equivalent to a salt-to-cellobiose molar ratio of 10). MgCl2 promotes both isomerization and hydrolysis reactions, as the yields of GF, GM and glucose all increase with MgCl2 concentration at low temperatures (i.e., <225 degreesC). However, at increased temperatures (i.e., 250 degreesC), the disappearance of three primary products also increases with MgCl2 concentration. As the MgCl2 concentration increases from 14.5 to 29 mM, the catalytic effect of MgCl2 on cellobiose primary decomposition is limited. However, increasing MgCl2 loading still significantly affects the secondary decomposition of GF, GM and glucose, leading to their rapid disappearance. As a result, the maximal yields of GF and GM are increased but the maximal glucose yield is reduced. The selectivities of GF and GM also increase but that of glucose decreases with increasing MgCl2 concentration, suggesting the promotion effect of MgCl2 on isomerization reactions becomes stronger at higher MgCl2 concentrations. However, such promotion effects become limited when the MgCl2 concentration further increases from 14.5 to 29 mM. Unlike GF and GM which have decreasing selectivities as cellobiose conversion increases, the glucose selectivity increases as cellobiose conversion increases, albeit becomes slower at a higher MgCl2 concentration due to secondary decomposition of glucose. The rate constant of cellobiose decomposition does not increase linearly with MgCl2 concentration, suggesting that the catalytic effect is not due to Mg2+. It seems that some other species play important roles to catalyse the cellobiose hydrothermal decomposition. Further analysis was then done to calculate the concentrations of all Mg-containing ions in the MgCl2 solution at various initial concentration and temperature conditions, such as Mg2+, MgCl+ and Mg(OH)+. It is interesting to find out that the rate constant of cellobiose decomposition is linearly proportional to the Mg(OH)+ concentration at various temperatures. Such a finding is important as it clearly indicates that Mg(OH)+ is the active species to catalyse the cellobiose decomposition in HCW, particularly the isomerization reactions. The promotion of hydrolysis reaction in the MgCl2 solution is due to the increased concentration of H+ from the hydrolysis of Mg2+ at increased concentrations.

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)0.001
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 score0.125
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
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 head0.007
GPT teacher head0.232
Teacher spread0.225 · 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".

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Citations0
Published2015
Admission routes1
Has abstractyes

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