SinoTechIntel Academic Portal
Open AccessDOI: 10.1007/s12613-025-3154-2Original Research

CO2 nanobubble-enhanced cement–fly ash backfill: Optimizing aggregate gradation and microstructure

Xiaoxiao Cao¹,Haoyan Lyu¹,Yanlong Chen¹,Jiangyu Wu¹,Hideki Shimada¹,Takashi Sasaoka¹,Akihiro Hamanaka¹

Laboratory of Rock Engineering & Mining Machinery, Department of Earth Resources Engineering, Kyushu University, Fukuoka 8190395, Japan; State Key Laboratory of Intelligent Construction and Healthy Operation and Maintenance of Deep Earth Engineering, China University of Mining and Technology, Xuzhou 221116, China

Read Executive PreviewQuick FAQ
CO2 nanobubble-enhanced cement–fly ash backfill: Optimizing aggregate gradation and microstructure
Graphical Abstract / Figure
Published In
Journal of Mineral Metallurgy and Materials Science
Published:December 2, 2025Edition:Vol. 32, Issue 12 • pp. 539-551Citation:Xiaoxiao Cao et al. (2025), Journal of Mineral Metallurgy and Materials Science
Impact Factor3.5 (Q2 - USTB)
Sponsored Research Partner
Keywords & Index Terms:CO2 nanobubblecement–fly ash backfillfractal dimensionaggregate gradationmicrostructurecarbon sequestrationmechanical propertiesgreen mining

Key Takeaways & Executive Findings

  • • Synergistic optimization of fractal aggregate grading (dimension 2.65) and CO2 nanobubble technology significantly increases CFB density and mechanical performance, with uniaxial compressive strength and elastic modulus improving by up to 13.46% and 27.47%, respectively. • CO2 nanobubble water enhances hydration and carbonization, promoting the formation of C–S–H and C–A–S–H gels and CaCO3, which strengthen the microstructure of cement–fly ash-based backfill. • The CO2 sequestration capacity of cement–fly ash-based materials treated with CO2 nanobubble water increases by 12.4 wt% to 99.8 wt% across different fractal dimensions, supporting low-carbon mine filling. • The research provides a scientific basis for designing high-performance, low-carbon backfill materials and advancing sustainable green mining practices.
Sponsored Research Highlight

Abstract

Mine filling materials urgently need to improve mechanical properties and achieve low-carbon transformation. This study explores the mechanism of the synergistic effect of optimizing aggregate fractal grading and introducing CO2 nanobubble technology to improve the performance of cement–fly ash-based backfill materials (CFB). The properties including fluidity, setting time, uniaxial compressive strength, elastic modulus, porosity, microstructure and CO2 storage performance were systematically studied through methods such as fluidity evaluation, time test, uniaxial compression test, mercury intrusion porosimetry (MIP), scanning electron microscopy-energy dispersive spectroscopy analysis (SEM-EDS), and thermogravimetric-differential thermogravimetric analysis (TG-DTG). The experimental results show that the density and strength of the material are significantly improved under the synergistic effect of fractal dimension and CO2 nanobubbles. When the fractal dimension reaches 2.65, the mass ratio of coarse and fine aggregates reaches the optimal balance, and the structural density is greatly improved at the same time. At this time, the uniaxial compressive strength and elastic modulus reach their peak values, with increases of up to 13.46% and 27.47%, respectively. CO2 nanobubbles enhance the material properties by promoting hydration reaction and carbonization. At the microscopic level, CO2 nanobubble water promotes the formation of C–S–H (hydrated calcium silicate), C–A–S–H (hydrated calcium aluminium silicate) gel and CaCO3, which is the main way to enhance the performance. Thermogravimetric studies have shown that when the fractal dimension is 2.65, the dehydration of hydration products and the decarbonization process of CaCO3 are most obvious, and CO2 nanobubble water promotes the carbonization reaction, making it surpass the natural state. The CO2 sequestration quality of cement–fly ash-based materials treated with CO2 nanobubble water at different fractal dimensions increased by 12.4wt% to 99.8wt%. The results not only provide scientific insights for the design and implementation of low-carbon filling materials, but also provide a solid theoretical basis for strengthening green mining practices and promoting sustainable resource utilization.

1. Introduction

As global climate change escalates and the need for natural resources rises, mitigating carbon emissions and harnessing waste resources have emerged as critical issues for environmental conservation and sustainable development. Cement–fly ash-based backfill materials (CFB) provide an eco-friendly, cost-effective alternative for mine filling. CFB lowers material expenses and carbon emissions by partially substituting cement with industrial waste fly ash, simultaneously improving structural integrity by filling goaf. Nevertheless, fly ash exhibits poor reactivity, and when its percentage escalates, the mechanical qualities and durability of CFB frequently deteriorate, constraining its application in intricate engineering contexts. Consequently, enhancing macroscopic mechanical characteristics and optimizing microstructure by scientific design and sophisticated modification technologies is a contemporary research priority.

In recent years, mine filling technology, as an important means of solid waste resource utilization and ecological environment protection, has been widely used in goaf support, surface settlement control and reduction of waste rock storage. Typical filling materials such as cement–fly ash-based mixtures are widely used due to their good fluidity and mechanical properties. To improve mechanical strength and fracture toughness, some studies have introduced fiber reinforcement technology and used X-shaped rock mass to reinforce broken pillars. In addition, the combination of tailings from different ore types and crushed stones with varying particle sizes has also been proven to significantly affect the microstructure and strength evolution behavior of the filling body. In mine backfill materials, the composition of aggregate gradation significantly affects the compaction and mechanical properties of cementitious fill materials. Conventional grading design often depends on empirical formulae or screening curves. Recent progress in fractal dimension-based aggregate grading offers a more empirical basis for aggregate design, precisely characterizing the complexity of particle dispersion and enhancing aggregate packing density.

SinoTechIntel Interactive Document Reader
Page 1–5 of Preview
100%
Download Full PDF

Loading authentic research manuscript (Pages 1–5)...

Sponsored Research Partner
Cite This Research Paper
Xiaoxiao Cao, Haoyan Lyu, Yanlong Chen, Jiangyu Wu, Hideki Shimada, Takashi Sasaoka, Akihiro Hamanaka (2025). CO2 nanobubble-enhanced cement–fly ash backfill: Optimizing aggregate gradation and microstructure. Journal of Mineral Metallurgy and Materials Science. https://doi.org/10.1007/s12613-025-3154-2
SinoTechIntel Academic & Legal Disclaimer

Research & Educational Purpose Only:The translations, structured abstracts, analytical annotations, and data reports provided by SinoTechIntel are intended exclusively for academic research, internal corporate R&D, and educational benchmarking. They do not constitute formal engineering, chemical safety, legal, or professional advice.

Copyright & Intellectual Property Notice: Original copyright of the underlying source articles and experimental data remains with the respective authors, institutions, and original publishing journals. SinoTechIntel claims intellectual property only over its proprietary translations, analytical syntheses, and AEO structured enhancements in accordance with international fair use and academic citation principles.

Frequently Asked Questions

What are cement–fly ash-based backfill materials?

Cement–fly ash-based backfill materials (CFB) are eco-friendly mine filling materials that partially substitute cement with industrial waste fly ash. They lower material costs and carbon emissions while improving structural integrity by filling goaf.

How does CO2 nanobubble technology improve backfill performance?

CO2 nanobubble water promotes hydration and carbonization reactions, leading to increased formation of C–S–H and C–A–S–H gels and CaCO3. This enhances the mechanical strength and microstructure of the backfill material, while also improving CO2 sequestration.

What is the optimal fractal dimension for aggregate gradation in this study?

A fractal dimension of 2.65 was found to provide the optimal balance between coarse and fine aggregates, significantly improving structural density and yielding peak uniaxial compressive strength and elastic modulus.

How much does CO2 sequestration increase with this approach?

The CO2 sequestration quality of cement–fly ash-based materials treated with CO2 nanobubble water increased by 12.4 wt% to 99.8 wt% across different fractal dimensions, demonstrating strong potential for low-carbon backfill applications.

What are the practical implications of this research?

The findings provide scientific insights for designing and implementing low-carbon filling materials and offer a solid theoretical basis for strengthening green mining practices and promoting sustainable resource utilization.

Recommended Scientific Literature & Research Partners

Related Technical Papers & Translations

Research Paper
Design and optimization of a high-efficiency distillation process for cellulosic fuel ethanol integrated with thermal coupling and molecular sieve adsorption

Design and optimization of a high-efficiency distillation process for cellulosic fuel ethanol integrated with thermal coupling and molecular sieve adsorption

To address the challenges of high energy consumption and prominent costs in the traditional three-columns distillation process for cellulosic fuel ethanol, a distillation—molecular sieve coupling separation process is proposed. This process integrates a three-column (crude distillation column, first distillation column, second distillation column) system with a 3A molecular sieve adsorption deep dehydration unit. A thermal coupling network is constructed via differential pressure design (steam from medium/high-pressure columns as mutual heat sources, reboiler liquid waste heat for feed preheating), and molecular sieve adsorption conditions are optimized. The study first performs a thermodynamic consistency test on the ethanol—water system, determines optimal non-random two-liquid (NRTL) model binary interaction parameters via experimental data regression for Aspen Plus simulation. Aiming at minimum total annual cost (TAC), Aspen Plus is used to optimize process parameters (theoretical tray number, feed location, reflux ratio, side-draw position, etc.). Economic analysis shows this process reduces CO2 emission costs by 27.56%, TAC by 15.58% (to 5.123 × 106 USD·a-1), and increases ethanol purity to >99.6%, providing an effective solution for green, efficient separation.

Read Abstract & PDF
Research Paper
A cohesion loss model for determining residual strength of deep bedded sandstone

A cohesion loss model for determining residual strength of deep bedded sandstone

Rock residual strength, as an important input parameter, plays an indispensable role in proposing the reasonable and scientific scheme about stope design, underground tunnel excavation and stability evaluation of deep chambers. Therefore, previous residual strength models of rocks established were reviewed. And corresponding related problems were stated. Subsequently, starting from the effects of bedding and whole life-cycle evolution process, series of triaxial mechanical tests of deep bedded s

Read Abstract & PDF
Research Paper
Federated model with contrastive learning and adaptive control variates for human activity recognition

Federated model with contrastive learning and adaptive control variates for human activity recognition

Recent attention to privacy issues demands a communication-safe method for training human activity recognition (HAR) models on client activity data. Federated learning (FL) has become a compelling technique to facilitate model training between the server and clients while preserving data privacy. However, classical FL methods often assume independent and identically distributed (IID) data among clients. This assumption does not hold true in practical scenarios. Human activity in real-world scena

Read Abstract & PDF