Nanostructured foam ceramics for  building purposes: production technology and applications

Sinitsin, D.A.; Shayakhmetov, U. Sh.; Rakhimova, O.N.; Khalikov, R. M.; Недосеко, И В

doi:10.15828/2075-8545-2021-13-4-213-221

Cited by 2 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Twenty promising catalyst candidates were found based on the optimization. In addition, with the development of nanotechnology, new materials are constantly being discovered and explored [25][26][27][28][29][30][31][32][33][34][35][36][37]. These pose the more demanding challenges in predicting material properties which require higher precision and faster speed.…”

Section: Introductionmentioning

confidence: 99%

Applications of machine learning in computational nanotechnology

Liu¹,

Wu²,

Yang³

et al. 2022

Nanotechnology

View full text Add to dashboard Cite

Machine learning (ML) has gained extensive attentions in recent years due to its powerful data analysis capabilities. It has been successfully applied to many fields and helped the researchers to achieve several major theoretical and applied breakthroughs. Some of the notable applications in the field of computational nanotechnology are machine learning potentials, property prediction and material discovery. This review summarizes of the state-of-the-art research progress in these three fields. Machine learning potentials bridge the efficiency vs. accuracy gap between density functional calculations (DFT) and classical molecular dynamics (MD). For property predictions, machine learning provides a robust method that eliminate the needs of repetitive calculations for different simulation setup. Material design and drug discovery assisted by machine learning greatly reduces the capital and time investment by orders of magnitude. In this perspective, several common machine learning potentials and machine learning models are firstly introduced. Using these state-of-the-art models, developments in property predictions and material discovery are overviewed, respectively. Finally, this paper was concluded with an outlook on future directions of data-driven research activities in computational nanotechnology.

show abstract

Section: Introductionmentioning

confidence: 99%

Applications of machine learning in computational nanotechnology

Liu¹,

Wu²,

Yang³

et al. 2022

Nanotechnology

View full text Add to dashboard Cite

show abstract

“…To measure the thermal conductivity coefficient of foam gypsum samples with dimensions of 250•250•50 mm, the ITP-MG4 "100" device was used: the test range is 0.03-1 W / (m• о C), the limit of the relative error in determining the thermal conductivity coefficient is ± 7. The determination of the thermal conductivity of a foam gypsum sample provides for the creation of a constant heat flow through the sample at a fixed temperature difference on its surfaces [6].…”

Section: Introductionmentioning

confidence: 99%

Effective use of dry foaming agents in the manufacture of foam gypsum thermal insulation nanocompositions

Shigapov¹,

Sinitsin²,

Khalikov³

et al. 2022

Nanobuild

Self Cite

View full text Add to dashboard Cite

Introduction. The elaboration of foamed eco-friendly gypsum nanocomposites with low specific gravity, high thermal insulation, operational and technical and economic characteristics remains an urgent task. Controlled hardening of gypsum foam nanoassemblers using dry foaming agents is a promising direction in the technology of production of heat-insulating building materials. Methods and materials. The production of a foam gypsum composition was carried out in an ejector-turbulent mixer by mixing a gypsum nanobinder with functional foaming nanoadditives. Building gypsum G5 BII was used as a binder in the work; porization of gypsum compositions was carried out using an adsorbed foaming agent PBNS. Results. The transformation of liquid-phase foaming agents into solid-phase ones by binding water allows the production of gypsum foam compositions from dry mixtures, which gives high dosing accuracy, a good degree of homogenization of components and stable characteristics of foamed gypsum nanocompositions. In production conditions, a one-storey small-sized pavilion was built using a pilot 3D printer AMT S1160, in which vertical enclosing structures are filled with foamed heat-insulating nanostructured foam gypsum. Monolithic foam gypsum nanomaterial with a density of 300-400 kg/m 3 was used for insulation and sound insulation of attic floors during the overhaul of the historic building of the Veterans Hospital in Ufa. Discussion. The elaboration of technology for obtaining foam gypsum from dry mixtures is based on the advantage of manufacturing and using thermal insulation nanocompositions, which allows for significant punctuality of dosing and stable characteristics of foam gypsum building materials. Surfactants have a significant effect on the kinetics of the structure formation of the foam gypsum nanocomposition and slow down the coalescence of air bubbles. Conclusions. Nanoporous foam gypsum concrete, obtained as a result of controlled hardening, with a density of 400 kg/m 3 has a thermal conductivity of 0.12 W/(m•ºС) and a compressive strength of 1.4 MPa. The compressive strength of foamed foam gypsum using a dry foaming agent on sorbents is 17% higher than the strength of a heat-insulating nanomaterial prepared using traditional technology.

show abstract

Nanostructured foam ceramics for building purposes: production technology and applications

Cited by 2 publications

References 0 publications

Applications of machine learning in computational nanotechnology

Applications of machine learning in computational nanotechnology

Effective use of dry foaming agents in the manufacture of foam gypsum thermal insulation nanocompositions

Contact Info

Product

Resources

About