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Record W2010974538 · doi:10.1039/c4ra10408k

A multiple-assembly/one-pot-crystallization strategy for a relatively more eco-friendly synthesis of hydrothermally stable mesoporous aluminosilicates

2014· article· en· W2010974538 on OpenAlexfundno aff
Qingxun Hu, Junsu Jin, Chunyan Xu, Xionghou Gao, Honghai Liu, Lan Ling, Xiaoliang Yuan

Bibliographic record

VenueRSC Advances · 2014
Typearticle
Languageen
FieldMaterials Science
TopicMesoporous Materials and Catalysis
Canadian institutionsnot available
FundersUniversity of British Columbia
KeywordsMesoporous materialCrystallizationRaw materialChemical engineeringEnvironmentally friendlyZeoliteAluminosilicateMaterials scienceHydrothermal synthesisYield (engineering)Mother liquorHydrothermal circulationCatalysisChemistryOrganic chemistryMetallurgy

Abstract

fetched live from OpenAlex

Mesoporous aluminosilicates (MA) with high hydrothermal stability obtained by assembly of zeolite Y precursors are attractive for the potential application in heavy oil catalytic cracking. A relatively more eco-friendly synthesis of MA with low synthesis cost and waste water discharge is particularly appealing. We report on the green synthesis of MA via a multiple-assembly/one-pot-crystallization (MA/OC) process by recycling the non-reacted reagents in the assembly mother liquor after separating the assembly solid product. This approach was achieved by exact supplementary compensation of the consumed raw materials and correct pH adjustment for the assembly liquor after each cycle of assembly. The assembly solid products in 5 cycles were collected and crystallized in one-pot in the mother liquor. Characterization results indicated that consumption of P123 and water discharge of MAOC-5 could be reduced to 51.5% and 27.3% of those of conventional MA-1. Meanwhile, the product yield of MAOC-5 is 102.66 g L−1, 4.8 times that of MA-1. This strategy suggests a relatively more eco-friendly and low cost route for the synthesis of hydrothermally stable MA.

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)0.001
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 score0.025
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.016
GPT teacher head0.258
Teacher spread0.242 · 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

Citations7
Published2014
Admission routes1
Has abstractyes

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