In vitro Arthrographis kalrae biofilm formation: Scanning electron microscopy and cytotoxic analysis

Souza, Bianca Dorana de Oliveira; Arcos, Janneth Josefina Escobar; Nakamura, Celso Vataru; Nagashima, Luciene Airy; Sano, Ayako; Ono, Mário Augusto; Itano, Eiko Nakagawa

doi:10.1016/j.jmii.2021.11.002

Cited by 1 publication

(1 citation statement)

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“…Additionally, Image J software was utilized to conduct grayscale analysis of the microstructure, in order to compare the compactness of different SCPB samples. Furthermore, SEM was utilized to observe the hydration products in SCPB, which aided in analyzing the degree of hydration reactions in different samples [39,40].…”

Section: Methodsmentioning

confidence: 99%

Strength Investigation and Prediction of Superfine Tailings Cemented Paste Backfill Based on Experiments and Intelligent Methods

Zhang

et al. 2023

Materials

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The utilization of solid waste for filling mining presents substantial economic and environmental advantages, making it the primary focus of current filling mining technology development. To enhance the mechanical properties of superfine tailings cemented paste backfill (SCPB), this study conducted response surface methodology experiments to investigate the impact of various factors on the strength of SCPB, including the composite cementitious material, consisting of cement and slag powder, and the tailings’ grain size. Additionally, various microanalysis techniques were used to investigate the microstructure of SCPB and the development mechanisms of its hydration products. Furthermore, machine learning was utilized to predict the strength of SCPB under multi-factor effects. The findings reveal that the combined effect of slag powder dosage and slurry mass fraction has the most significant influence on strength, while the coupling effect of slurry mass fraction and underflow productivity has the lowest impact on strength. Moreover, SCPB with 20% slag powder has the highest amount of hydration products and the most complete structure. When compared to other commonly used prediction models, the long-short term memory neural network (LSTM) constructed in this study had the highest prediction accuracy for SCPB strength under multi-factor conditions, with root mean square error (RMSE), correlation coefficient (R), and variance account for (VAF) reaching 0.1396, 0.9131, and 81.8747, respectively. By optimizing the LSTM using the sparrow search algorithm (SSA), the RMSE, R, and VAF improved by 88.6%, 9.4%, and 21.9%, respectively. The research results can provide guidance for the efficient filling of superfine tailings.

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Section: Methodsmentioning

confidence: 99%

Strength Investigation and Prediction of Superfine Tailings Cemented Paste Backfill Based on Experiments and Intelligent Methods

Zhang

et al. 2023

Materials

View full text Add to dashboard Cite

show abstract

In vitro Arthrographis kalrae biofilm formation: Scanning electron microscopy and cytotoxic analysis

Cited by 1 publication

References 10 publications

Strength Investigation and Prediction of Superfine Tailings Cemented Paste Backfill Based on Experiments and Intelligent Methods

Strength Investigation and Prediction of Superfine Tailings Cemented Paste Backfill Based on Experiments and Intelligent Methods

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