The cattle breeding system is facing severe problems associated with the increased negative impact of various human activity areas on the environment and the bodies of farm animals. The use of heavy metals in different production areas leads to their accumulation in the environment due to the ingestion of animals and humans through animal products. This study aimed to assess the elemental composition of the hair and milk of black-spotted cows and to identify the relationship between the content of toxic and essential elements and the state of the intestinal microbiome.The element status was estimated by studying the chemical composition of the biosubstrates using inductively coupled plasma-mass spectroscopy. Based on the analysis of hair, the elemental composition, and the use of the coefficient of toxic load, two groups of animals were formed: Group I, which included cows with a lower load factor, and Group II, which included cows with a higher load factor.An increase in the heavy metal concentrations in the hair and milk of animals in Group II was observed. The As, Fe, Pb, Al, Co, Ni, and V concentrations in the hair of cows from Group II increased relative to Group I by 19%, 29%, 24.5%, 32.3%, 35.6%, 21.5%, and 18.2%, respectively. There was a significant increase in the level of Fe by 11.5%, Cr by 8.25%, Mn by 17.6%, Pb by 46.1%, and Cd by 25% in Group II compared with Group I in the assessment of elemental milk composition. There were no apparent changes in the intestinal microbiome of Group II.Some heavy metals were accumulated in the bodies and milk of animals. This shows a high probability of heavy metals causing harm to the health of animals and humans. In recent decades, dairy farming has been gaining momentum in producing milk and dairy products worldwide , 2. DairHair is a material frequently used to assess the elemental status of animals. The use of hair as an indicative biomaterial is associated with its informative value as a long-term parameter for determining the state of mineral metabolism , 17. TheThis study aimed to assess the elemental composition of the hair and milk of black-spotted cows. In addition, this study aimed to identify the relationship between the content of toxic and essential elements and the state of the intestinal microbiome.The Ethics Committee of the Federal Research Centre for Biological Systems and Agrotechnologies of the Russian Academy of Sciences approved the experimental design (No. 5 of 02/05/2021). All animal studies were performed following the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments.This study was conducted from June to August 2021. Experiments were conducted at the center of collective sharing of Biological Systems and Agrotechnologies of the Russian Academy of Sciences.The studies were conducted on a model of black-spotted cows  in JSC “Kalinina.” . All animals were clinically healthy. All animals were under the same conditions and pasture management. The diet of the black-spotted cows during the experiment is shown in Animal hair and milk samples were used as biosubstrates to study elemental status. Hair samples weighing at least 0.4 g were taken from the upper part of the animals’ withers according to the standard technique. The proximal portion of hair was cut off 15 mm long from the root within the period closest to sampling to assess the elemental status of the organism.The collected hair samples were washed in acetone  within 10–15 min. Then, the hairs were washed thrice in deionized water (18 MΩ • cm). Deionized water was obtained using an electric distiller with a combined membrane set DVS-M/1HA-1(2)-L . Later, samples were dried at 60°C until they reached an air-dry state. The corresponding sample weights (about 0.05 g) were treated with 5 mL of nitric acid (Khimmed) in a Multiwave 3000 microwave system  using the following mode: The temperature was raised to 200°C for 5 min and remained stable for 5 min at 200°C. Then, the temperature was cooled to 45°C. The split solutions were poured into 15 mL polypropylene tubes. Labels and caps were washed three times with deionized water. The rinsed samples were included in the appropriate tubes. Finally, the solutions were filled up to 15 mL with deionized water and thoroughly mixed by shaking closed tubes .3 (Sigma-Aldrich) in distilled deionized water (18 MΩ cm-1)  [Raw milk samples were taken individually from each cow, placed in sterile containers, and cooled to 5°C. The obtained samples were diluted  with an acidified (pH = 2.0) diluent consisting (v/v) of 1% 1-butanol , 0.1% Triton X-100 , and 0.07% HNOMA, USA) .Analytical studies of the microelement composition of hair and milk according to 25 elements  were conducted in the laboratory of the ANO Center for Biotic Medicine (Moscow). The atomic emission  and mass spectral  analyses were conducted with inductively coupled plasma.tox) was calculated to assess the magnitude of the toxic load on the cows’ bodies. The sum of the coefficients of some heavy elements  was used to calculate the coefficient of the toxic load (Ktox):The coefficient of the total toxic load , five animals were selected from each group to study the intestinal microbiome. However, the apparent limitation of our study was the small number of animals included in the analysis. This occurred due to the design of the main research and the remarkably high cost. Nevertheless, these data provide new insights into the intestinal microbiome. The study of the microbiome was conducted in the large intestine (gut content) of cows. Samples were taken with a sterile instrument into a test tube with a preservative solution . Finally, the samples were frozen.After dividing the total number of cows into groups according to KThe samples were homogenized on a TissueLyser LT  with a Lysing Matrix Y . DNA extraction from samples was performed using a QIAamp Fast DNA Stool Mini Kit (Qiagen) according to the manufacturer’s instructions. The quality of the extracted DNA was assessed using electrophoresis in 1% agarose gel and a Nanodrop 8000 . The DNA concentration was quantified using a Qubit 4 Fluorometer  with a dsDNA High Sensitivity Assay Kit (Life Technologies).Preparation of the DNA libraries was made according to the Illumina protocol  with primers targeting the V3–V4 regions of the SSU ribosomal RNA (rRNA) gene, S-D-Bact-0341-b-S-17 (5′-CCTACGGGNGGCWGCAG-3′) as the forward primer and S-D-Bact-0785-a-A-21 (5′-GACTACHVGGGTATCTAATCC-3′) as the reverse primer. The reaction mixture (25 μL) contained both primers (0.2 μM of each). The mixture also contained 80 μM dNTPs and 0.2 U Q5 High-Fidelity DNA Polymerase . The following PCR program was used: 95°C for 3 min, 25 cycles: 95°C for 30 s, 56°C for 30 s, 72°C for 30 s, and final extension 72°C for 5 min. The DNA libraries were cleaned using Agencourt AMPure XP beads . They were validated by capillary electrophoresis on a Qiaxcel Advanced System (Qiagen) using the QIAxcel DNA Screening Kit (Qiagen). Paired-end 2 × 251 bp sequencing was performed on the MiSeq platform  with the Reagent Kit v.2 .DNA library preparation, sequencing, and bioinformatics treatment were performed in the Center of Shared Scientific Equipment “Persistence of Microorganisms” from the Institute for Cellular and Intracellular Symbiosis UrB RAS .In the first stage, the raw reads obtained as a result of sequencing were evaluated using FastQC v. 0.11.7 . The evaluation was necessary to determine the parameters of further processing and included an assessment of the quality and length of reads and the presence of adapter sequences. Paired-end reads were merged using USEARCH v10.0.240_win32 (drive5.com/usearch), using the “-fastq_mergepairs” command with the “-fastq_maxdiffs 10 -fastq_pctid 80 options.” After merging and adapter removal, the reads were re-evaluated with FastQC v. 0.11.7. Subsequent treatment of merged reads was conducted with Usearch v10.0.240_win32 (drive5.com/usearch) [The next stage included dereplication and clustering of the filtered reads using the UPARSE algorithm. As a result of dereplication and clustering, operational taxonomic units (OTUs) were formed. Chimeric sequences were detected and removed using the UCHIME2 algorithm . The visThe data were processed using the methods of variation statistics and the statistical package Statistica 10 . The differences regarding the data on the taxonomic composition of the intestinal microbiome were considered statistically significant at p ≤ 0.05 using Student’s t-test.th quartiles (Q25–Q75).For data on the elemental composition of hair and milk, compliance with the normal distribution law was verified using the Kolmogorov goodness-of-fit test. The hypothesis that the data belonged to a normal distribution were rejected in all cases, with a probability of 95%. Therefore, the use of nonparametric procedures for statistically processing populations was justified, allowing the use of the Mann–Whitney U test. The data obtained are shown as median (Me) and 25–75tox calculation, two groups were formed. Group I included cows with a lower load of Ktox <8.95 relative to the studied sample. Group II included cows with a higher load factor Ktox >8.95 relative to the study sample. Significant differences in According to the Ktox was revealed by the content evaluation of chemical elements in cows’ hair. The As, Fe, Pb, Al, Co, Ni, and V concentrations in the hair of cows from Group II have significantly increased relative to Group I by 19%, 29%, 24.5%, 32.3%, 35.6%, 21.5%, and 18.2%, respectively. In addition, an increase in the level of several other elements  was observed in experimental Group II in comparison with group I. A decrease in the concentration of the elements Ca, K, Na, P, Se, B, and Sr was the exception when Groups I and II were compared.The regularity of the increase in the average values of individual element concentrations with the increase in KThere were no significant differences between the experimental groups for most of the studied elements of the composition of the cows’ milk, according to the analysis of the elements . At the There were significant Spearman correlations between changes in the content of several chemical elements in hair and those in milk . There wThere was a high diversity of taxonomic groups in the analysis of the intestinal microbiota of cows. As a result of sequencing, 301,938 reads were obtained, from 26,613 to 33,808 initial reads per sample. After the merging and filtering steps, 253,319 reads were included in the analysis. After clustering, a total of 516 OTUs were obtained. After removing singletons and doublets from the samples, 496 OTUs remained. It should be noted that 21 OTUs were found in only one of the ten samples. Therefore, they were removed from further analysis.Based on the obtained sequences and OTUs, the resolution curves were plotted. The resolution curves of all samples tended to plateau to a maximum, indicating the sufficiency of the sequencing depth for characterizing ruminal microbiota in this study . The resRuminococcaceae (46.1%), unclassified Bacteroidales (13%), Lachnospiraceae , and Bacteroidaceae (8.35%). The most abundant classified microorganisms at the genus level belonged to unclassified Ruminococcaceae (41.6%) and unclassified Bacteroidales (13%). A similar ratio of the main taxonomic groups of microorganisms of the intestinal microbiota was observed in samples of cows in Group II. There was a significant decrease in the number of microorganisms belonging to the class Gammaproteobacteria (Ruminobacter) by 2% (p = 8.82) (Firmicutes (32.1%) and Bacteroidetes (60.1%) were the most numerous phyla in samples from cows in Group I. Proteobacteria (3.98%) and Spirochaetes (1.37%) were among the least frequent phyla . At deep = 8.82) .Considering the alpha diversity calculations, it was possible to assess the richness, diversity, and homogeneity of the intestinal microbiota of cows in experimental Groups I and II . The valThe intake of heavy metals in the bodies of farm animals is one of the most critical issues in modern agricultural production. Heavy metal accumulation in soil caused by human economic activities leads to the inevitable entry of these elements into the bodies of farm animals. Therefore, it is necessary to assess the condition of animals and food products of animal origin , 24.tox values. Discrepancies in Ktox values are probably more due to differences in the level of metabolic processes.The content analysis of chemical elements in the hair of cows made it possible to divide the experimental animals into two groups characterized by high and low K25–Q75 percentile intervals) was recommended to obtain the range of optimal values of the content of chemical elements. It was shown that there were similar deviations for the animals in both groups in this analysis [tox, a more pronounced deviation in the content of chemical elements in hair from the recommended physiological range was observed compared to animals from the group with low Ktox. Nevertheless, such discrepancies in the content of individual elements in the hair of cows with the recommended physiological standard were probably due to more differences in the geographical zones of animal habitation, the level of metabolic processes, age, and the genotype of animals [A comparison of the content of chemical elements in the hair of cows from Groups I and II . The prerequisites for an increase in the number of Ruminobacter species, which destroy polysaccharides in the gastrointestinal tract of cattle, are probably due to the ability of some heavy metals to reduce the activity of gastric enzymes, such as alpha-amylase [Among the significant changes in the intestinal microbiome of Group II cows, there was a tendency to increase the number of bacteria belonging to the class Gammaproteobacteria  between changes in toxic metal concentrations in hair and milk. A similar pattern was observed in other studies. An increase in the content of Cd and Pb in the blood and urine of animals is significantly correlated with an increase amounting to these metals in milk , 36.The observed increase in the concentration of several toxic elements in the hair and milk of animals from Group II requires clarification about the probable sources of heavy metals and the reasons for their accumulation in the bodies of the analyzed animals. As part of the study, there was no artificial introduction of supplements with a high concentration of several heavy metals in the diet. In this regard, the most probable sources for the production of heavy metals were water and fodder obtained under free-range conditions (pasture maintenance). A significant correlation between the levels of some elements  in animal feed, water, and milk has been noticed in previous studies . DespiteThe probable reason for the accumulation of certain metals in the body of some cows under the same conditions of keeping may be differences in the level of metabolic processes in the body. For example, it was noted in previous studies that elements such as Cu, Mn, and Zn accumulate in higher concentrations in multiparous cows compared to primiparous cows . The intAt the same time, the metabolic activity of the rumen and intestine microbiomes of cows is also one of the factors of differences in metabolic processes in their bodies. The metabolic outcome of toxic substances from the environment may be altered by the microbiome of the gastrointestinal tract, altering the absorption rate of these substances . In addiWe observed an increase in the concentration of heavy metals in the hair and milk of animals in Group II. However, we did not observe significant changes in the concentrations of most of the essential elements  in animal milk. In contrast, their content in animal hair was reduced. We also did not observe reorganization signs of the intestinal microbiome in animals with an increased coefficient of toxic load. Changes in the metabolism of elements in the organism of animals in Group II were not critical. A more extended period of exposure would probably be required for the appearance of apparent shifts in the mineral composition of milk. At the same time, a high probability of a negative impact on the health of animals and humans may be shown by the tendency to accumulate heavy metals in hair and milk. In this regard, it is necessary to monitor the state of animals further and dynamically observe a broader range of animal health indicators. Such indicators include analysis of the activity of metal-specific proteins in the blood of animals, metabolic pathways of the intestinal microbiome, and the relationship between levels of oxidative stress and changes in the metabolic pool of micro and macronutrients.Authors can confirm that all relevant data are included in the article and/or its supplementary information files. The authors declare that the data supporting the findings of this study are available within the article Miroshnikov SA, Skalny AV, Zavyalov OA, Frolov AN, Grabeklis AR (2020) The Reference Values of Hair Content of Trace Elements in Dairy Cows of Holstein Breed. Biol Trace Elem Res Mar 194:145-151. https://doi.org/10.1007/s12011-019-01768-6.All authors contributed to the study’s conception and design. TK and OM: Material preparation, data collection, and analysis. EY, YK, ES, and SL: Drafted the manuscript. All authors revised the manuscript and approved the final manuscript.


The vast amount of epidemiologic and genomic data that were gathered as a global response to the COVID-19 pandemic that was caused by SARS-CoV-2 offer a unique opportunity to shed light on the structural evolution of coronaviruses and in particular on the spike (S) glycoprotein, which mediates virus entry into the host cell by binding to the human ACE2 receptor. Herein, we carry out an investigation into the dynamic properties of the S glycoprotein, focusing on the much more transmissible Delta and Omicron variants. Notwithstanding the great number of mutations that have accumulated, particularly in the Omicron S glycoprotein, our data clearly showed the conservation of some structural and dynamic elements, such as the global motion of the receptor binding domain (RBD). However, our studies also revealed structural and dynamic alterations that were concentrated in the aa 627–635 region, on a small region of the receptor binding motif (aa 483–485), and the so-called “fusion-peptide proximal region”. In particular, these last two S regions are known to be involved in the human receptor ACE2 recognition and membrane fusion. Our structural evidence, therefore, is likely involved in the observed different transmissibility of these S mutants. Finally, we highlighted the role of glycans in the increased RBD flexibility of the monomer in the up conformation of Omicron. In 2020, an infection outbreak began in China that was responsible for human respiratory symptoms and was caused by a so far unknown viral strain, named Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) ,2,3.The Coronaviridae family, a group of enveloped, positive-sense, single-stranded RNA (+ssRNA) viruses that includes another two viruses that are known to cause high pathogenic zoonotic infections: SARS-CoV-1 (detected in 2002–2003), which belongs to the same species of SARS-CoV-2 and Middle-East respiratory syndrome coronavirus (MERS-CoV), that was first isolated in 2012 and that is associated only occasionally to human–human transmission events [SARS-CoV-2 is a member of the n events . The genn events .−3 substitutions per site per year, corresponding to one substitution every 11 days [Since the first isolate MN908947.3), that was collected from COVID-19 patients in Hubei province of China in December 2019, SARS-CoV-2 has been evolving at a mutation rate estimated by 1.1 × 10.3, that  11 days ,13. As t 11 days , the sup 11 days  were dow 11 days , and all 11 days  as well Since its first identification, multiple vaccines that are based on different technologies and several therapeutics against SARS-CoV-2 and its associated COVID-19 disease have been successfully developed in a concerted global effort to provide effective protection against severe illness ,18,19,20We first investigated the impact of the mutations that had accumulated in the Delta  and OmicThe larger fluctuations in the Delta variant, as in WT, are in the receptor binding motif (RBM) region of the receptor binding domain (RBD), i.e., the region with direct interactions with the Angiotensin-Converting Enzyme 2 (ACE2) human receptor and in the N-terminal domain (NTD). Both regions in Delta, however, have higher maximum values than in WT . The monomer in the up conformation (monomer 2), showed an increase in its fluctuation both in NTD and in the P631 region, at the C-term of subdomain 1 (S1), which is one of the two subunits that compose the S protein see A. The fiThe peak of fluctuations in subdomain 2 (S2), that are located in G832 of monomer 2 in WT, was maintained in Delta, but with the highest fluctuation located in residue C840 of monomer 3.Of note is the slight reduction of fluctuations in the residue P681R, the first residue of the canonical furin-like cleavage site (FCS) PRRAR↓S sequence in Delta as compared to WT.More differences in RMSF are observed in Omicron as compared to WT, as to be expected by its higher number of mutations. The region with the highest fluctuations is the NTD. Here, the increase in fluctuations is found in the two peaks that were already observed in WT and Delta (comparison of E156/D253 in Omicron with R158/L249 in WT and K147/S256 in Delta). We highlight that the peak in D253 involves the monomer in an up-conformation. The RBD is not the region with higher fluctuations in Omicron, but the motion is more distributed along the whole region and also involves monomer 2. The FCS region shows fluctuations that are comparable to the WT (comparison of S686 in Omicron with P681 in WT) even though P681 is mutated to a histidine. A moderate increase in fluctuations is observed in the S2 peak (comparison of D839 in Omicron with G832 in WT and C840 in Delta).To play its biological role, the S glycoprotein must undergo numerous conformational changes, such as RBD rotation in an up-conformation; host-receptor binding; and proteolytic processing at the S1/S2 and/or S2′ sites, close to the fusion peptide, for virus-cell fusion . The EssAfter concatenating the trajectories of the three monomers, we performed the ED analyses for Delta, WT, and Omicron, filtering the new trajectories along the eigenvectors with higher eigenvalues. A great portion of the total protein motion in these molecular variants is described by the two main eigenvectors V1 and V2  showed a reduction of RMSF along V1 as compared to the WT. Conversely, the close D839, in the FPPR, showed a large increase in RMSF along V1. The peak that was centered on the residue F157 is due to its deletion in the Delta variant. Therefore, the negative value in The relative conservation of the motion along V1 of Omicron as compared to WT, notwithstanding its higher number of variants, was unexpected. The negative peak at Y144 was due to the deletion of this residue in the Omicron S glycoprotein. Analogously, the positive peak in the first inserted residue  with D138, N137, L141, and S162. All these interactions are lost by the Delta mutation in Gly158, and the only hydrogen bond with approximately 25% of the residence time is formed between the main chain of G158 and E154, in monomer 1 and 3. On the contrary, the Delta mutation of P681R produces a very stable hydrogen bond in monomer 1 (84% of residence time) with E661 and more flexible interactions with Q675. On the other hand, this mutation seems to have less influence on the interactions of this residue when the monomer is in the up configuration.Looking at the interactions that are lost by mutants for deleted residues or gained by Omicron for its insertion, the general trend is maintained, with an average number of hydrogen bonds lost per deleted residue of 0.77 in Delta vs. 0.47 in Omicron, considering also that this last value includes the highest absolute value, i.e., 2.1 hydrogen bonds lost by N211 in monomer 2 that in WT have stable interactions with F186 and K182. Also, the contribution in terms of gained hydrogen bonds by the three inserted residues in Omicron is not distinguishably high, being 0.48.Overall, the comparison between Delta and Omicron makes us speculate that the S protein might tolerate a few mutations with high impact or several milder ones, in order to maintain its structural biological role.The N-linked glycans of SARS-CoV-2 S protein are essential for the structure and function of the protein. The glycans are fundamental for the correct folding of the protein, shield the epitopes from the antibody recognition and modulate the structure and dynamics of the S protein . MD simuIn order to investigate the effect of mutations in Delta and Omicron variants on the structure and dynamics of S proteins, we analysed the changes induced by mutations on the glycan-glycan and glycan-protein interactions involved in the modulation of the opening of RBD domain. In particular, we analysed the stability of the hydrogen bond network formed by the glycans close to RBD in “up” conformation. The stability of the glycan-glycan and glycan-protein hydrogen bonds was evaluated in terms of the fraction of MD time in which the hydrogen bond is formed with respect to the MD production time.In Delta and Omicron variants, as well as in WT, the glycan at N165 (FA2G2S1) of monomer 3 makes stable hydrogen bonds with the amino acid of the RBD domain of monomer 2 in up conformation .In the Omicron variant, the glycan at N165 interacts also with the RBD domain of the monomer 1 for more than 82% of the MD production time. Specifically, the glycan at N165 makes stable hydrogen bonds with Y498 (24.5% of residence time), G499 (59.1% of residence time), G501 (10.1% of residence time), and H502 (85.1% of residence time).The glycan that is linked to N234 of the monomer 3 (Man9) does not interact with the RBD domain of monomer 2 in the WT, Delta, and Omicron proteins. In the Delta variant, this glycan makes hydrogen bonds with the amino acids of RBD domain of monomer 3 (intermonomer hydrogen bonds) during all the MD production time (99.9% of residence time). In the Omicron variant, the glycan at N234 forms hydrogen bonds not only with the RBD domain of monomer 3 (69% of the MD production time) but also with the central helix of the monomer 1 (97.7% of residence time). Specifically, the glycan at N234 makes stable hydrogen bonds with S746 (80.7%), E748 (100%) and with T747, N751, N978, F981, N985, and K986 for more than 35% of the MD production time .The glycan that is linked to N343 of monomer 3 (FA2) plays a fundamental role in the mechanism of opening the RBD domain of SARS-CoV2 S protein . In our In the Delta variant, the interactions between glycan at N343 of the monomer 3 and the glycans at N165 and N234 of the same monomer persist A even ifIn the case of the Omicron variant, the glycan at N343 of the monomer 3 does not interact with the glycan N165 and N234 of the same monomer . In thisAs previously reported , other gIn the Delta variant both the glycans at N122 and N149 make stable hydrogen bonds with the RBD domain in up conformation for more than 97% of the MD production time . The glyIn the case of the Omicron variant, the glycansthat are linked to N122 and N149 interact through hydrogen bonds, with the RBD domain of monomer 2 with a residence time of 97.6% and 72.4%, respectively. The glycan that is linked to N122 makes stable hydrogen bonds with the glycan N331 and N343 of monomer 2 (above 91% of residence time) whereas the interaction of the glycan at N149 with the glycan N331 and N343 appears weaker with respect to N122 with a residence time of 18% and 77%, respectively.The RBD domain plays a crucial role in the interaction with cellular receptors, leading to viral infection. In particular, internal to the RBD domain is the RBM region, which contains residues that are responsible for direct interaction with the ACE2 receptor. To further investigate the impact of Delta and Omicron mutations on this region, we performed secondary structure analyses for the three monomers in the three molecular systems.Among the zones with less conformational stability, we can highlight the regions around aa 477 and 487 that do not have preserved structural patterns either between monomers or between variants. The plasticity of this region was already shown by the MD simulation of the Alpha variant, where we observed the switch of the 479–484 beta-sheet in the up monomer of WT to an alpha-helix conformation .In the present study, we have investigated the structural and dynamic effects of the nine mutated residues in the S protein of Delta in comparison to the effects that the 39 mutations in Omicron, ten of which are in the RBM, bring to the S glycoprotein. RMSF analysis reveals that the major changes in the Delta S protein, as in WT, are concentrated in the important RBM region that is responsible for the interaction with ACE2 and also in the N-terminal region. Surprisingly, although the Omicron variant has 15 mutations in the RBD region, the average per residue fluctuations are similar to those that were found in theWT, and the biggest differences are in the N-terminal region. Overall, the RMSF  WT peaksThe hydrogen bond analyses that were performed  point ouWe demonstrate that glycans play an essential role in modulating the observed S plasticity. A different network of glycan–protein interactions , in factThe secondary structure analysis reveals small variation in the RBD region of Delta and Omicron as compared to the WT , where tOur results suggest that the complex structural changes that allow binding and entering the host cell require great resilience to the general behavior of the S protein. On the other hand, the inherent plasticity of some regions and subtle changes that are induced by the mutations allows for adaptation to the host that might explain the observed extensive evolution and emergence of the SARS-CoV-2 variants.It is worth noting that coherent structural results highlight the role of specific S regions, i.e., a.a. 624–629, the PRRA cleavage site, and the FPPR region. These regions, in fact, show high correlated motion along V2 in the SARS-CoV-2 ancestor S glycoproteins from bat and pangolin , Delta, Our evidence points out that the structural alterations that are generated by Delta mutations are not very different from those of the Omicron, which makes us believe that, in general, the structure of the S protein can support few more drastic mutations or multiple milder ones, to maintain its biological role. On the other hand, several mutations, even of minor impact from the structural point of view, can modify the glycan network shell, culminating in altering the flexibility of the opening dynamics of the variant and possibly in its interaction with the ACE2 receptor. For the modeling of the S glycoproteins, we considered the protocol that was described by Borocci et al. . Brieflywww.glycam.org, accessed on 2 December 2021).The three modeled structures were glycosylated following the procedure that was described by Borocci et al.  using th−1 nm−2 on the protein backbone and glycans to avoid close contacts. After this first minimization protocol, the models were added to a cubic box, solvated with water, and neutralized with ions using the genion GROMACS tool. A second minimization step was then performed for the three systems followed by 5 ns of MD simulation by applying positional restraints of 1000 kJ mol−1 nm−2 to the protein atoms. After, unrestrained MD simulations were carried out with a time step of 2 fs for a length of 1 µs. The P-LINCS [After the glycosylation of the three models, the S protein was minimized in vacuum with harmonic restraints of 1000 kJ mol P-LINCS  algorith P-LINCS  to keep  P-LINCS  was empl P-LINCS  algorith P-LINCS  force fi P-LINCS  force fi P-LINCS , respect P-LINCS  2020.6 oThe RMSF analyses were performed with the GROMACS rmsf tool and plotted with grace software. To maintain the WT numbering, dummy residues were inserted into the plot. The Essential Dynamics analyses were calculated on c-alpha atoms on the concatenated trajectories for the three monomers by GROMACS covar and anaeig tools, following the protocol that was described by Borocci et al. .Secondary structure data were generated by cpptrajtool  and plotThe cluster analysis was carried out on the WT, Delta, and Omicron trajectories using the GROMACS cluster tool, according to the algorithm that was described by Daura and coauthors . For allHydrogen bond analysis was carried out with VMD  and TCL MD analyses were carried out on a g100 CINECA supercomputer in the framework of the ELIXIR-IT HPC@CINECA program  and on t


Using iso-phthalic acid and m-phenylenediamine as monomers, the changes in the thermodynamic properties and microstructure parameters of SiO2-modified PMIA were analyzed using molecular dynamics before and after modification in the temperature range of 250~450 K. It was found that adding SiO2 improves the Young’s modulus and Shear modulus of PMIA, and the mechanical properties of PMIA, and SiO2/PMIA composites deteriorate with increasing temperature, but the mechanical properties of SiO2/PMIA composites are always better than those of pure PMIA in the temperature range of electrical equipment. Meanwhile, after doping SiO2 with the radius of 8 Å, the glass transition temperature of PMIA increases by 27.11 K, and its thermal conductivity increases from 0.249 W m−1 K−1 to 0.396 W m−1 K−1. When SiO2 is added to PMIA, the thermal expansion coefficient of PMIA will decrease in both glass and rubber states, and its thermal stability will improve. In terms of microstructure parameters, the free volume distribution of the SiO2/PMIA model is less easily dispersed than that of the PMIA model, indicating that the addition of SiO2 can improve the related properties of PMIA by hindering the movement of molecular chains.The poly-m-phenyleneisophthalamide (PMIA) is widely used in the electrical field due to its numerous favorable characteristics, but its poor thermal conductivity limits its application. In this study, PMIA was modified with nano-silica (SiO As the most critical and expensive equipment in the power system, the health of the transformer is an important guarantee of the economic operation, and the safety and stability of the power grid . The ins−1 K−1 [Aramid paper series products play an important role in insulation materials, including meta-aramid paper, aramid-mica paper, and aramid soft composite materials . Among t−1 K−1 , making 2O3, BN, carbon nanotubes, graphene, etc., to the polymer matrix [2 filler is cheap and has excellent thermal and mechanical properties. When it is added into polymer, the composites formed not only have high insulation strength, but also have high thermal conductivity and heat resistance of inorganic filler [2 can improve the thermal conductivity and mechanical properties of polymer materials, but the mechanism by which SiO2 improves the thermodynamic properties of PMIA needs further study.At present, there are two main approaches to improving the thermal conductivity of polymers. One is to improve the intrinsic thermal conductivity of polymers from the polymer matrix. The second is to prepare polymer composites by adding high thermal conductivity fillers, such as Al, Alr matrix ,8. Amongc filler . Lihua Wc filler . Zhikun c filler . It was Molecular dynamics (MD) simulations have been used to study the behavior of molecules at the atomic/molecular level, which can be very convenient to study the changes in the microstructural parameters of materials, establish the relationship between micro and macroscopic, and provide a more reasonable explanation of the interaction mechanism between fillers and substrates ,13.2-doped aramid composite (SiO2/PMIA) were established using molecular dynamics simulation. The changes in thermal conductivity, glass transition temperature, thermal expansion coefficient, and the mechanical properties of PMIA doped with SiO2 were studied and analyzed, and the macroscopic properties were explained by microscopic parameters such as free volume and radial distribution function. At the same time, the influence of temperature on the properties of meta-aramid and SiO2/PMIA composites in the temperature range of power equipment was studied, which laid a foundation for the subsequent preparation of high thermal conductivity aramid materials suitable for the power system.In this paper, the models of PMIA and SiOModels were built in Materials Studio (MS) software . All simPMIA is a polymer with a molecular weight of 60,000–90,000 formed by spatial intricate polymerization of Iso-phthalic Acid and m-Phenylenediamine monomers . The mol2 nano-cluster model by Build module in the MS software, the surface of the SiO2 molecule was oxidized, that is, the broken bond on the Si atom on the surface is combined with -OH, and the broken bond on the O atom on the surface is combined with -H atom. The radius of SiO2 nanoclusters is set to 8 Å. The initial configuration of SiO2 crystal is not stable, so it is necessary to perform the geometric optimization in the Forcite module of the MS software to obtain its low-energy morphology.Nano-silica is characterized by high surface activity, poor stability, unsaturated residual bonds, and highly polar hydroxyl groups ,20. When2-doped PMIA/SiO2 composite crystal cell were established by the Amorphous Cell module of the MS software, as shown in 3, but to avoid the penetration and connection between aramid molecular chains during the establishment of single cells, a lower initial density should be selected so that the molecular chains have enough space to twist [3, it takes too long to perform simulation under NPT ensemble several times to increase the density. Set the initial density to 0.6 g/cm3, set the pressure to 0.15 GPa, and 100 ps simulation under NPT ensemble is carried out to make the cell density close to the experimental value.PMIA crystal cell and SiOto twist . After sThe Focite module is selected to optimize the geometry of the model, to obtain its low-energy configuration. Then, to make the hole distribution of the crystal cell closer to the real material and overcome the potential well between the local minimums of the system, in the Focite module, the Anneal task is selected to carry out 25 cyclic annealing treatments from 800 K to 300 K, and then to 800 K. After the above treatment, the local unreasonable structure in the amorphous model is eliminated, making the molecular unit cell more balanced and stable, which provides a reasonable geometric conformation for the next simulation calculation.The COMPASS force field is used in all modeling and geometric optimization processes. This force field has proved to be the first molecular force field that unifies organic and inorganic molecules into one force field . The AndJ is the energy flux in the z direction, dT/dz is the temperature gradient, and the minus sign indicates that the direction of the energy flux is opposite to the gradient.The equilibrium model is simulated by the RNEMD (Reverse Non-Equilibrium Molecular Dynamics)  method wT at the interface is obtained by fitting the linear part of the temperature in the polymer region. Finally, the interfacial thermal conductivity is calculated by Formula (2):Then, a 500 ps RNEMD simulation under NVT ensemble is carried out. In this paper, the time step of all simulation processes is 0.1 fs. The first 250 ps is used for system equilibrium, and the last 250 ps is used for result analysis. The temperature in this time range is counted for 10 times (50 ps/time), and the mathematical average of the 10 results is calculated. The temperature drop ΔA represents cross-sectional area at the interface; 2/PMIA models for calculating thermal conductivity separately, according to the previous model construction method. When a stable temperature gradient is formed in the system, the model temperature changes along the z direction as shown in To divide the layers conveniently, the model is expanded about three times along the z direction in a molecular dynamics calculation, so it is necessary to establish the PMIA and SiO2 modification. The thermal conductivity of pure PMIA is 0.249 W m−1 K−1, and the experimental value of thermal conductivity of PMIA is about 0.24 W m−1 K−1 from the literature [The results in terature . The com2 radius, the thermal conductivity first increases and then decreases, reaching the maximum value when the radius is 8 Å. At this time, the thermal conductivity of SiO2/PMIA is 0.396 W m−1 K−1, which is 59.04% higher than that of pure PMIA.It can be seen from The object will shrink and expand with the change of temperature. The coefficient of thermal expansion (CTE) refers to the ratio of the length change in one direction of a solid substance to its length at room temperature when the temperature changes by 1 K . For mosTg) is the boundary point between the glassy state and the rubber state of materials, which can be used as a characteristic index to characterize polymers. The density, volume, and other related parameters change greatly before and after the glass transition temperature. The volume and temperature of the system before and after the glass transition temperature are fitted to obtain the corresponding temperature volume curve, as shown in 2/PMIA systems is obtained from the fitting curve, and then the thermal expansion coefficients of the glass state (below Tg) and the rubber state (above Tg) of each system are calculated through Formulas (3) and (4).The glass transition temperature σi=CiIt is found that the elastic strain of most materials is symmetrical, that is, E and Shear modulus G of material mechanical parameters can be obtained from Equations (7) and (8) respectively.Lamé constant 2/PMIA models at 250 K, 300 K, 350 K, 400 K, and 450 K after cooling simulation were selected for geometric optimization again, and the optimized unit cells were selected to eliminate the internal stress of the system as much as possible. After geometrical optimization, the NPT dynamics of the model is calculated for 50 ps again; one model is output every 1 ps, until finally 50 models are output. The five models with the lowest potential energy are selected to calculate their mechanical properties. The constant strain method is selected for calculation, and the maximum strain is 0.003. Finally, the sum of Lamé constants of the PMIA and SiO2/PMIA models at different temperatures is obtained, and then five groups of mechanical parameters of corresponding models are obtained from equations, and their average values are taken as mechanical parameters at each temperature. 2/PMIA material with temperature.The PMIA and SiO2, which shows that adding SiO2 can enhance the rigidity of aramid material and make it less prone to deformation. As the temperature increases, the Young’s modulus and Shear modulus of pure PMIA and SiO2/PMIA composites decreased. The Young’s modulus of pure PMIA at 450 K decreases by 16.8% compared with that of 250 K, and the Shear modulus decreased by 16.8%. However, the Young’s modulus and Shear modulus of SiO2/PMIA composites decreased by 20.9% and 22.2%, respectively. The stiffness of PMIA and SiO2/PMIA composites decreased with the increase of temperature, and the mechanical properties of SiO2/PMIA composites were more affected by the increase of temperature. However, the mechanical properties of PMIAs with added SiO2 were always better than those of pure PMIAs in the temperature range of electrical equipment.It can be seen from According to the free volume theory, the holes between molecules in materials are called free volumes, and the space occupied by molecular chains is called occupied volumes . Free vo2/PMIA models at different temperatures are shown in the table. 2/PMIA models at different temperatures (blue part). It can be seen from 2, the free volume distribution in the SiO2/PMIA model is not particularly dispersed with the increase of temperature, which further shows that adding SiO2 will hinder the movement of the aramid molecular chain.The free volumes and occupied volumes of the PMIA and SiO2, it increases the density of the system, which makes the material more tightly bound internally, reduces the gaps between the aramid molecular chains, and increases the resistance to movement, thus weakening the chain motion of the aramid molecular chains.When aramid is doped with SiOThe presence of hydrogen bonds is one of the reasons for the tight junctions of aramid fiber molecules. The addition of nano-silica, which contains a large number of hydroxyl oxygen atoms and molecules, easily combines with the -NH group and oxygen atoms in the aramid fiber, creating new hydrogen bonds, as shown in AN and BN are the number of atoms in groups A and B respectively, ABN is the number of atoms shared by the two groups of atoms, r is the radial distance coordinate, and atoms i and j are the atoms in groups A and B respectively. The Radial distribution function (RDF) refers to the probability of distribution of other particles in the space around a given particle, that is, the distance from other particles to a given particle. RDF can be used to study the ordering of materials from the structural characteristics of micro-reactive materials . The defIn 2/PMIA composite models are calculated. It can be seen from the figure that the general trend of all atom RDFs of the two models is the same and basically coincides. Because the proportion of oxygen atoms (O) and silicon atoms (Si) in the nano filler is small, it does not make a significant contribution to the corresponding peak, and there is only one peak at 3.2 Å.In 2 has no significant effect on the total atoms RDF of the system.It is worth noting that G(R) does not have any peak in the range greater than 3.8 Å and tends to be close to 1, which is generally considered as evidence that the polymer system has amorphous characteristics . In gene2-modified PMIA materials within the operating temperature range of the equipment, and obtains the following conclusions:Focusing on the aramid insulating materials used in power systems equipment, this paper studies the influence of temperature on the thermodynamic properties of PMIA and SiO2 in the PMIA can increase the thermal conductivity, glass transition temperature, Young’s modulus, and Shear modulus, and decrease the thermal expansion coefficient of the PMIA. It can be seen that the addition of SiO2 can improve the thermal conductivity, thermal properties, and mechanical properties of the PMIA.The addition of nano-SiO2/PMIA composites decreased by 20.9% and 22.2%, respectively. The mechanical properties of modified aramid composites deteriorated with the increase of temperature, but the mechanical properties of modified aramid composites were always better than those of pure PMIA composites in the range of the temperature rise of the casing.The Young’s modulus and Shear modulus of pure PMIA decreased by 16.8% at 450 K, compared with that at 250 K, while the Young’s modulus and Shear modulus of SiO2, the CTE of PMIA will decrease in both the glass and rubber states, and the thermal stability of the composite will be improved. This is because the addition of SiO2 will hinder the movement of the aramid molecular chain.The thermal stability of aramid materials in the glass state is better than that in the rubber state. After the addition of SiO2 hinders the movement of the molecular chain and changes the microstructure of the PMIA, which is reflected in the fact that the free volume distribution of the PMIA is less easily dispersed with the increase of temperature.The addition of SiO


In addition, as the stress ratio increased, non-Mg2Si phase fracture initiation points appeared in the BM. Meanwhile, the quantity of non-Mg2Si phases in the joints continued to increase, and the crack initiation sites became increasingly concentrated in the TMAZ-HAZ region.The fatigue crack initiation and growth characteristics in 7050-T7451 aluminum alloy butt joints subjected to different stress ratios and owing to friction stir welding (FSW) were investigated using fatigue tests for stress ratios of 0.1, 0.3, and 0.5. The difference between the fatigue crack initiation in the base material (BM) and FSW joints, related to coarse secondary phases, was explored using scanning electron microscopy (SEM). Accordingly, Al High-strength aluminum alloys have been widely used in the aerospace industry and high-speed vehicles owing to their low density and high strength-to-weight ratio . HoweverRecent studies have shown that coarse secondary phases play an important role in the fatigue behavior of aluminum alloys. Zheng et al.  investigAs demonstrated above, while there have been numerous studies on secondary phases of aluminum alloys, the mechanism of crack initiation and the effect of the stress ratio of the different secondary phases in the base material and joints are not well studied in a systematic manner.In this study, high-cycle fatigue tests were conducted on the aluminum alloy 7050-T7451 and its FSW joints, subjected to the stress ratios of 0.1, 0.3, and 0.5. The relationship between the coarse secondary phases and the preferred crack-initiation sites is discussed, and the irregular distribution of crack-initiation locations in joints subjected to various stress ratios is investigated.The BM was a rolled plate of the aluminum alloy 7050-T7451 with the dimensions of 12 mm × 1500 mm × 3000 mm. The chemical compositions and mechanical properties are listed in A hardness test across the entire weld joint was conducted on the middle line of the thickness, using an HV-1000A microhardness tester . The hardness test utilized an indent spacing of 0.15 mm for the TMAZ and SZ and 0.2 mm for the BM and HAZ, a load of 3 kgf, and a loading and holding time of 15 s. The specimens used for the hardness test were ground using a wet sandpaper to 7000 mesh before being polished with a diamond sus-pension  to remove the influence of the surface-hardening layer. Fatigue tests on specimens subjected to different stress ratios were conducted using a GPS200 high-frequency fatigue test machine in an ambient environment. The specimens of the 7075-T7451 aluminum alloy BM and FSW butt joints in 7 cycles, the fatigue strengths for the BM were 254, 228, and 175 MPa, respectively, whereas for the FSW joints, the conditional fatigue strengths were 193, 177, and 138 MPa, respectively. In addition, the fatigue strengths of the FSW joints were approximately 20% lower than those of the BMs subjected to the same stress ratios; in all the cases, the fatigue strengths decreased as the stress ratio increased. Test results are given in A comparison of the fatigue S–N curves for various stress ratios of the BM and joints is shown in The specimens for the optical microscopy (OM) analysis were cut perpendicular to the welding direction and prepared following the metallography procedures described in The welding process can have a significant impact on the microstructure, and large angular grain boundaries are dangerous locations for fatigue failure of the specimen . The mic2Si, while the white coarse secondary phases mainly consist of Al23CuFe4, Al7Cu2Fe, and Al2Mg3Zn3, based on the atomic number calibration analysis. In the BM, only the Mg2Si phase appears to be gray–black, while all the other non-Mg2Si phases are white. The coarse secondary phase in the BM is categorized into Mg2Si and non-Mg2Si phases, in agreement with the gray difference of the coarse secondary phase under the backscatter lens, to enable further statistical analysis.The coarse secondary phases were characterized via selected-area diffraction by TEM analysis, and the results are shown in 2Si phases, while both mechanisms coexist in the case of Mg2Si secondary phases. The mechanism of the fatigue crack emergence in the high-circumference range was the primary focus of this study. Because fatigue crack initiation and early growth account for most of the fatigue life, the impact of the FSW on the microstructure complicates the fatigue crack-initiation mechanism. Fine precipitates are common sites for fatigue crack nucleation in aluminum alloys. The constituent particle’s size and shape are important characteristics that influence crack nucleation. Additionally, differences in stiffness and thermal expansion coefficients between the inclusion and the surrounding matrix can introduce a localized stress concentration in and around a particle which increases the likelihood of fatigue crack formation. Fatigue cracks often form at inclusions by one of three mechanisms: inclusion cracking, debonding of the interface between the inclusion and matrix, or cracking at lines of slip in the surrounding matrix . Accordi2Si for the BM and Al23CuFe4 for the joints  cracking accounted for 32 of the 40 fractures in the joint specimens. The gray–black Mg2Si phase was the primary cracking phase for the BM specimens, whereas the white secondary phase was the primary cracking site for the joint specimens. Therefore, secondary phases are the predominant fatigue crack-initiation sites in the 7050-T7451 aluminum alloy, regardless of the BM and FSW joints.2Si, which was the only stable intermetallic phase in the Mg-Si binary alloy. Although the Mg2Si phase has excellent mechanical properties, it is highly brittle and has low ductility, as shown in 2Si on the fracture surface, which is fragmented and debonded from the matrix. 2Si debonding from the substrate and the serrated cracks at the edges, which indicates that cracks tended to initiate in the bulk Mg2Si, with the subsequent coalescence and deflection of microcracks along the interface leading to serrated cracks. Although the Si content in the 7050 aluminum alloy was only 0.10%, its size was similar to that of Al2CuMg, and most of the Mg2Si phase observed at the broad transverse side was internally broken. However, the non-Mg2Si phase was tightly bound to the matrix, as shown in The major cracking phase of the BM specimens was Mg2Si phase, which indicates that the crack initiation mechanism of the Mg2Si phase in the joints was the same as in the BM. The thermomechanical deformation of FSW might have worsened the bonding strength between the Mg2Si phase and the matrix. 2Si phase (50 μm), which was first subjected to external forces. Subsequently, the cracks gradually spread across the interface into the matrix as a result of the cyclic loading. As shown in 2Si phase can be divided into two distinct regions: (1) a smooth region created by instantaneous fracture close to the specimen surface and (2) a rough region with signs of stable crack extension far away from the specimen surface.The distribution of the coarse secondary phase on the fracture surface, for various phases of the fatigue crack extension, was investigated for determining the impact of the coarse secondary phase on the fatigue crack extension. The results are shown in 2Si and non-Mg2Si phases for the three stress ratios are listed in 2Si. However, as the stress ratio increased, non-Mg2Si phase cracks appeared. For the joint specimens, most of the cracked phase was the non-Mg2Si phase at low mean stress values, and this phenomenon became more pronounced as the stress ratio increased. In the BM, the stress concentration at the non-Mg2Si and matrix interface was insufficient to cause crack initiation when the stress was relatively low. For the FSW joints, the specimens were primarily broken in the non-Mg2Si phase, and internal cracking was more likely to extend to the matrix. This behavior became more obvious at higher stress ratios. 2, 432 μm2, and 378 μm2, and the average areas of the cracked phases for the joint specimens were 323 μm2, 293 μm2, and 288 μm2, respectively. The cracking phase areas for the BMs were greater than for the joint specimens and decreased noticeably as the stress ratio increased.The numbers of the cracking phases in the joint and BM specimens for the MgThe fracture-location statistical analysis was performed for all specimens, and only the results for the FSW specimens are plotted in 23CuFe4, Al7Cu2Fe, and Al2Mg3Zn3 were the preferred joint crack-initiation sites, whereas Mg2Si was the major fracture-initiation point of the parent material. Two distinct crack-nucleation mechanisms were identified: (1) crack nucleation in the bulk of the non-Mg2Si phases such as Al23CuFe4, Al7Cu2Fe, and Al2Mg3Zn3 and (2) crack initiation at the Mg2Si–matrix interface. The former mechanism accounted for 77.6% of the FSW joint specimens, whereas 83.3% of the cracks formed in the BM specimens owing to the latter mechanism. In addition, as the stress ratio increased, non-Mg2Si phase fracture initiation points appeared in the BM. Meanwhile, the number of non-Mg2Si phases in the joints continued to increase, and the crack initiation sites became increasingly concentrated in the TMAZ-HAZ region.This study determined that coarse secondary phases served as the main fatigue crack-initiation sites for the BM and FSW joints of the 7050-T7451 aluminum alloy. Fractographic analyses demonstrated that Al


Health capital investment is an integral aspect of human capital investment, and it is vitally important to improve residents' health by encouraging them to maintain insurance. This paper estimates the potential impact of particulate pollution (PM2.5) on health insurance buyers at the city level. Using PM2.5 as a representative air pollution indicator, we construct a threshold panel model and a spatial econometric model based on 2000–2019 panel data from 256 Chinese cities and the health production function to examine the impact mechanism through which PM2.5 pollution causes changes in the number of health insurance buyers. The results indicate that higher PM2.5 pollution significantly increases health insurance buyers in China. Considering the threshold effect, per capita GDP has a nonlinear relationship with an increasing marginal effect on the higher number of health insurance buyers. Due to spatial spillover effects, PM2.5 pollution has an additional impact on the number of health insurance buyers, indicating that a lack of awareness of the spatial correlation will result in underestimating the impact of PM2.5 pollution on residents' health. The robustness of adjacency and geographic distance matrices demonstrates that the regression results are robust and reliable. The findings of this study provide a practical reference for health insurers' development and policymakers' pollution control efforts. Although the rapidly growing economy in China is substantially improving national strength and citizens' quality of life, environmental challenges are accumulating due to extensive ongoing development. These years of accelerated development have generated frequent nationwide air pollution incidents that have placed residents at risk of health hazards, including higher morbidity and mortality rates, and inequal cognitive abilities and health resources, which are pre-existing concerns at all levels of society –4. Hazy Most developed countries have established complete health insurance systems. Additionally, people in developing countries are also increasingly concerned about national healthcare systems. However, researchers of healthcare systems in developing countries concentrate the majority of attention on the ‘supply side', for example, proposing improvements in the categorisation of the spectrum of disease by making it more scientific and proposing regulation of the misuse of new medical technologies that result in medical service overuse . NeverthWhile being related to climatic factors, air pollution originates from extensive economic development, low energy efficiency, and insufficient environmental governance. While China has made great advances in energy conservation and emissions reduction in recent years, PM2.5 concentration has reached beyond WHO levels overall, with 81% of the population living in areas where PM2.5 concentration standing above WHO levels . RecogniIn conclusion, existing research primarily concentrates on the correlation of air pollution with healthcare spending. As an important indicator reflecting the development of the health insurance sector, the number of health insurance buyers can be used as a basis for the government to strategically develop relevant policies for health insurance to improve healthcare services. With a growing standard of living and increasing awareness of the effect of air pollution, a rising number of individuals will choose to purchase insurance. We select the number of health insurance buyers as an explained variable to examine how it is affected by PM2.5 pollution to apprehend the role of health insurance, optimise premium payments, and improve insurance efficiency. How does PM2.5 pollution impact the number of health insurance buyers with the current circumstances of China's economy? Does PM2.5 pollution have any non-linear spillover effect on the number of health insurance buyers? Are there any spatial differences regarding the impact of PM2.5 pollution? Existing literature rarely examines these questions in a comprehensive and systematic manner; therefore, the impact of PM2.5 pollution on the number of health insurance buyers is of practical significance for improving national public health, contributing to a healthier economy and inspiring policymakers to refine the insurance industry regulation.The paper offers three potential contributions. First, from the research perspective, we construct a macro examination of the health production function (HPF) of PM2.5 pollution affecting the number of health insurance buyers at the level of prefecture cities, referencing existing literature to analyse spatiotemporal distributions and spatial spillover effects. Second, regarding research methods, we employ panel threshold and spatial econometric models to analyse the nonlinear and spatial spillover effects of PM2.5 on the number of health insurance buyers, enriching relevant empirical studies. Third, for data selection, this paper uses panel data from 256 cities in China from 2000 to 2019 as samples. Compared with provincial data, the research conclusions are more authentic and reliable.The remainder of this paper is organised as follows. Section 2 Literature review provides a discussion of existing literature. Section 3 discusses the model, variables, and data description. Section 4 elaborates on the empirical results and analyses, and Section 5 concludes.Since the 1970s, the world has been assailed by environmental pollution concurrent with global economic growth, causing researchers to focus on environmental and public health. Existing literature primarily analyses the health impact of air pollution, medical insurance, and the correlations between air pollution and insurance premiums.3 increase in SO2, NO2, and PM10 concentrations drives up the potential mortality rate of cardiovascular and cerebrovascular diseases by 0.4% (95% CI: 0.1%−0.8%), 1.3% (95% CI: 0.2%−2.4%), and 0.4% (95% CI: 0.2%−0.6%), respectively. Js et al.  and an SEM based on 2000–2019 panel data of 256 Chinese cities, using PM2.5 as the indicator of pollution and the macroeconomic HPF to analyse the mechanism of the impact of PM2.5 pollution on public health at the city level and relevant effects.To test the above research hypothesis, this paper first tests the effects of PM2.5 pollution on the number of health insurance buyers using the following basic model referencing Zhang . Add othyit denotes the number of health insurance buyers; PM2.5 denotes PM2.5 pollution; Xit denotes the relevant controlled variables, primarily including urbanisation level (urbanrate), local fiscal expenditure (fis_exp), park green area per capita (greenarea), the number of hospital beds per 1,000 people (hos_beds), the ratio of the population above 65 years of age (age), industrial structure (second_rate), and regional GDP per capita (gdp). The subscript i denotes different cities; t denotes different years; α0denotes intercept terms; κi and λt denote individual fixed effect and time fixed effect, respectively; and εit denotes the random error term of the model.where Second, we expect the impact of PM2.5 pollution on the number of health insurance buyers to be limited by the regional economic development level. When this exceeds a certain threshold, the number of health insurance buyers will significantly increase, so there may be a nonlinear effect of PM2.5 pollution on the health insurance buyers. In this regard, this paper studies the effect of PM2.5 on the number of health insurance buyers by building a panel threshold effects model based on Hansen's model . The thr2. When the threshold variable [[Inline Image]] is below the threshold value, and the impact coefficient of PM2.5 regarding the number of health insurance buyers isβ1. The above equation only considers a single-threshold value. If there are multiple threshold values, the equation can be further extended, and other variables can be employed as above. To analyse the impact of PM2.5 pollution on the number of health insurance buyers in a panel threshold model, we introduce the threshold variable of economic development level , in which, the higher the economic development level of a country or region, the higher the residents' quality of life and the higher its investment in health insurance.where [[Inline Image]] is the threshold value. When the threshold variable [[Inline Image]] exceeds threshold value [[Inline Image]], the impact coefficient of PM2.5 on the number of health insurance buyers is βFinally, to verify the spatial spillover effect of PM2.5 pollution on the number of health insurance buyers, we establish the following spatial econometric model (SEM) to introduce a spatial interaction term between PM2.5 concentration and control variables based on Equation 1:yit is the explained variable; α0 is a constant term; ρ is the spatial autoregressive coefficient; W is the normalised 256 × 256 spatial weight matrix; W is the coefficient of the spatial spillover effect; ϕ1 and ϕ2 are PM 2.5 pollution and the coefficients of the spatial spillover effect of the related control variable, respectively; and other variables are the same as in Equation 2. Model 3 includes the spatial interaction term of the number of health insurance buyers as the explained variable and the spatial lagged term of PM2.5 pollution as the explanatory variable; therefore, Model 3 is a spatial Durbin model (SDM).where This paper uses the econometric statistical software Stata 16 to conduct the empirical analysis, examining 256 prefecture-level cities in China from 2000 to 2019 as a sample to analyse the impact of PM2.5 on residents' health, as some cities were newly established, and a large amount of data are missing.num_insur) to examine the effect of PM2.5 pollution on residents' health. An increase in the number of health insurance buyers can effectively counteract health hazards of air pollution and improve residents' health. Basic urban employee health insurance buyers include those that purchase basic urban employee health insurance and basic urban and rural resident health insurance. Among them, basic health insurance for urban employees follows the regulations of the People's Republic of China which require insurance to be paid by both the employee and the employer, but in actuality, employees may be unwilling to pay or employers do not pay the premium. The provisions of the health insurance system for urban and rural residents follow the principle of voluntariness. The data are from the statistical yearbooks of each city and the China City Statistical Yearbook, and the recorded number of health insurance buyers in various cities.Considering that the effect of PM2.5 pollution on residents' health may be reflected by the number of health insurance buyers, this variable serves as the explained variable (pm25gm) of the 256 cities. The data are from the Atmospheric Composition Analysis Group at the Dalhousie University and are rasterised to match the vector-mapped mean concentration values of the 256 prefecture-level cities. Archsmith  and park green area per capita (greenarea) are 6.21 and 43.69, respectively, and the respective standard deviations are 2.78 and 76.61, indicating that healthcare level and urban environments also vary considerably from city to city. Other control variables also differ to some extent.The results in Considering the endogeneity of the model, it mainly comes from two aspects. The first is reverse causality, which should not exist in this model, because PM2.5 may increase the number of health insurance buyers, and the increase or decrease of health insurance buyers will not affect PM2.5. Second, due to the different specific conditions of each city, there may be endogenous problems caused by missing variables. Existing research on PM2.5 pollution and public health does not usually consider the economic factors involved in environmental health concerns, resulting in a lack of relevance and effectiveness of environmental and health policy. Based on Grossman's HPF, we further introduce economic, social, and environmental factors to explore the impact of PM2.5 pollution on the residents' health insurance purchases at the urban level.(-1)](b-B) = 301.17 and p = 0.000; therefore, the original null hypothesis that individual and explanatory variables are not correlated must be rejected and a fixed-effects model is adopted.Our empirical analysis is conducted using the econometric statistical software Stata 16. First, we apply the Hausman test  to deterThe regression results in second_rate) has a significantly positive effect on the number of health insurance buyers. The economic development level (gdp) raises the upper limit of the population's healthcare budget for healthcare, and the number of health insurance buyers increases accordingly. The number of beds per 1,000 people (hos_beds) reflects the number of medical resources in the city and has a significant negative impact on the number of health insurance buyers. Larger park green area per capita (greenarea) indicates cities' higher greening rate and a better environment because green plants can effectively adsorb particulates in the air and release oxygen through photosynthesis, which is highly beneficial to residents' health and has a significant negative relationship with the number of health insurance buyers. In addition, the effect of regional financial expenditure (fis_exp) on the number of health insurance buyers, although positive, is not significant, indicating that the effect is not significant.The estimates of the control variables demonstrate that industrial structure (gdp) as the threshold variable to analyse the effect of PM2.5 pollution on the number of health insurance buyers. The next step is to determine the number of threshold variables, and the bootstrap method is employed to derive the p-value and the corresponding F-value. The results are presented in To further analyse whether the impact of per capita GDP on the number of health insurance buyers has a non-linear effect of the marginal increase, we must first determine whether there is a threshold effect, and before applying the threshold model, we first use the bootstrap sampling method to test the existence of the panel threshold and determine the number of thresholds. According to our previous theoretical analyses, there may be a nonlinear effect between PM2.5 pollution and the number of health insurance buyers. To reduce the error, this paper adopts Hansen's TPM and uses GDP per capita  plot is shown below , presentgdp < 6.459) and d2 (gdp >6.459). The effect of GDP per capita on the number of health insurance buyers is tested based on these intervals. The estimates of the threshold effect in gdp < 6.459, GDP per capita has a significant negative effect on the number of health insurance buyers, indicating that the residents in less developed cities are less willing to buy insurance. The effect of GDP per capita on the number of health insurance buyers is significantly positive when gdp >6.459, demonstrating that the buyer number has a non-linear characteristic of marginal increase as per capita income increases and reaches a certain threshold value, which indicates that residents are more concerned about physical and mental health and possess the economic strength to spend on healthcare. As income continues to increase, the positive effect of GDP per capita on the number of insured residents increases.The effect of PM2.5 on the number of health insurance buyers in China varies depending on the threshold values; therefore, we generate dummy variables based on the threshold values and interaction terms, which regressed. Based on the results of the threshold effect test, we divide GDP per capita into two intervals of d1 (num_insur) and the Moran's I of PM2.5 pollution are both >0. Although there are some fluctuations, all data are significantly positive at the 1% level, indicating that PM2.5 pollution and the number of health insurance buyers have positive spatial autocorrelation, where the global Moran's I interval of the health insurance buyer number is 0.140–0.180 and the global Moran's I interval of PM2.5 pollution is 0.010–0.055, indicating that the spatial autocorrelation of the number of health insurance buyers is stronger than PM2.5, the aggregation of PM2.5 pollution is poorer, and there is a spatial dependence and spatial heterogeneity between the two  of each city in China from 2000 to 2019. In the univariate spatial correlation test , the numTo further examine the aggregation characteristics of the explanatory and explained variables in the local areas, we calculate the local Moran's I of the number of health insurance buyers and PM2.5 pollution. The local aggregation characteristics indicate a positive spatial correlation , and theThe results of the global and local Moran's I demonstrate significant spatial correlations; therefore, the application of ordinary panel regression will result in estimated deviations and an SEM should be used. Furthermore, we used LR and Wald tests to ascertain whether the SDM can be degraded to a spatial lag (SAR) or a spatial error model (SEM). The test results are presented in pm25gm) and the number of health insurance buyers at the 1% significance level. This indicates that PM2.5 pollution leads to an increase in the number of health insurance buyers. The spatial autoregressive coefficient rho is positive and significant at the 1% level, indicating a significant regional spillover effect of PM2.5 pollution.Given the previous assessment, this paper focuses on the results of the SDM, and rho) mirror previous results, confirming robustness and reliability.The previous analysis examined the effect of PM2.5 pollution on the number of health insurance buyers under the economic distance matrix, and the results indicate that PM2.5 pollution can significantly increase the number of health insurance buyers. To further verify the robustness of the regression results, adjacency and geographic distance matrices are used. As shown in To further analyse the spillover effect of PM2.5 pollution on the number of health insurance buyers in local and neighbouring regions, this paper estimates the direct, indirect, and total effects of the explanatory variables on the explained variables. The direct effect refers to the spillover effect of the explanatory variable in the local area on the dependent variable, the indirect effect refers to the spillover effect of the explanatory variable in the local area on the explained variable in the neighbouring areas, and the total effect refers to the sum of both. The estimated results are presented in The regression results on the number of health insurance buyers indicate that the direct effect of PM2.5 pollution on the number of health insurance buyers is 0.018, which is significantly positive at the 5% level. Moreover, because of the spatial lag of all explanatory variables, the direct effect also includes the feedback effect of neighbouring areas, indicating the influence of the explanatory variables of neighbouring areas on the number of health insurance buyers in the local area. The feedback effect can be directly obtained based on the differences between the third column in The significant aim of health insurance is to protect residents' health and prevent tremendous health costs . CurrentAccording to the results, as the core explanatory variable, PM2.5 has a significant impact on the number of health insurance buyers. As the space factor may have spillover effects in terms of the impact of PM2.5 pollution on the number of health insurance buyers, cities should work collaboratively to address atmospheric pollution. Additionally, the secondary industry has a positive impact on the number of health insurance buyers because the consumption of tremendous quantities of fossil energy involves extremely severe pollution; therefore, the higher the proportion of the secondary industry, the greater the environmental stress. Residents will spend more to stay healthy and be more inclined to maintain health insurance. At the same time, rises in residents' income concurrent with rapid economic growth will generate higher inclinations to maintain health insurance.The environment is one of the most critical issues concerning public health, which, in combination with health insurance, is connected to national well-being. There is currently very limited research regarding the influencing factors and levels of health insurance buyers in China, particularly research analysing specific pollution sources such as PM2.5. Based on the data from 2000 to 2019 in 256 Chinese cities, this paper empirically verifies the impact of PM2.5 pollution on the number of health insurance buyers. The relevant results are 3-fold. (1) The estimation of the static panel model demonstrates that the significant environmental pollutant of PM2.5 has significantly increased the number of health insurance buyers. The more serious the air pollution, the higher residents' willingness to buy insurance, indicating a significant positive correlation. (2) The increase of GDP per capita on the number of health insurance buyers has a non-linear relationship with an increasing marginal effect. This paper uses per capita GDP as the threshold variable. When GDP <6.459, per capita GDP has a significant negative impact on the number of health insurance buyers, whereas when GDP > 6.459, the per capita GDP has a significant positive impact on the number of health insurance buyers. (3) The spatial econometric analysis demonstrates that PM2.5 pollution can increase the number of health insurance buyers in the local city with a significant spatial spillover effect, indicating that PM2.5 pollution in a local city has a significant positive effect on the number of health insurance buyers in neighbouring cities.Based on the empirical analyses above, the paper proposes three countermeasures. First, because environmental pollution contributes significantly to the growth of the number of health insurance buyers, the government should persistently focus on atmospheric pollution control action plans by codifying laws on environmental protection, energy conservation and emissions reduction, regulating corporate production and operational activities, levying pollution tax as soon as practicable, and limiting the excessive growth of insurance premiums. Insurers can introduce green insurance to strengthen environmental pollution risk management, minimise pollution incidents, and safeguard the interests of those affected by environmental pollution. It is essential to make the most of the internet to control pollution, conserve energy, reduce emissions, and optimise resource allocation efficiency.Second, the government should continue to vigorously develop the economy and enhance public health awareness. The impact of PM2.5 pollution on the number of health insurance buyers has a non-linear relationship with an increasing marginal effect. When the per capita GDP is less than the threshold of ¥64,590, the public will not have a strong willingness to buy health insurance. When the per capita GDP exceeds the threshold value, willingness to buy insurance and the number of buyers will continuously increase. Only continued vigorous economic development, improving incomes, and enhancing public health awareness will increase the number of individuals willing to buy health insurance to ensure public health.Third, air pollution control requires mutual cooperation and collaborative governance among different cities. The number of health insurance buyers has an autocorrelation with PM2.5 pollution at the city level. According to the global and local Moran's I, the concentration of PM2.5 pollution is poor, while the number of health insurance buyers is relatively strong. The impact of the core explanatory variable, PM2.5 pollution, will have a long and dynamic influence on the number of health insurance buyers. According to the autocorrelation and the lag verification, the influence of PM2.5 pollution has a significant spatial spillover effect on the number of health insurance buyers; therefore, both local city efforts and mutual cooperation and collaborative governance of all neighbouring cities are needed to prevent and control air pollution. Cities with more developed economies can have a leading influence by providing technology and funds to help underdeveloped cities eliminate and upgrade outdated industries to achieve coordinated development in the region.It is true that there are some deficiencies in predicting the development of the medical insurance industry through PM2.5. When selecting the control variables, only the aging population is considered, and children are also relatively vulnerable to pollution. Due to the unavailability of data, this paper does not use them. In addition, education level and consumption characteristics are also one of the factors affecting the number of health insurance buyers. To this end, we propose future research topics. First, the research adopts a variety of variables for measurement and analysis. For the data that is difficult to obtain from the database, the survey method can be used. Second, to further analyse the impact mechanism of PM2.5 on medical insurance.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.YS: conceptualization, methodology, formal analysis, writing—original draft preparation, and funding acquisition. TX: data processing, formal analysis, and writing—original draft preparation. All authors contributed to the article and approved the submitted version.This research was funded by the financial support of the National Social Science Foundation  and Innovative talents in philosophy and social sciences in universities and colleges in Henan Province (Project No: 2022-CXRC-29).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


This study investigated the effects of nano-silica (NS) and silica fume (SF) on the hydration reaction of high-volume fly ash cement (HVFC) composites. In order to solve the dispersibility problem caused by the agglomeration of NS powder, NS and NSF solutions were prepared. NS content and SF content were used as main variables, and an HVFC paste was prepared in which 50% of the cement volume was replaced by fly ash (FA). The initial heat of hydration was measured using isothermal calorimetry to analyze the effects of NS and SF on the initial hydration properties of the HVFC. In addition, the compressive strength was analyzed by age. The refinement of the pore structure by the nanomaterial was analyzed using mercury intrusion porosimetry (MIP). The results show that the addition of NS and SF shortened the setting time and induction period by accelerating the initial hydration reaction of HVFC composites and improved the compressive strength during the initial stage of hydration. In addition, the micropore structure was improved by the pozzolanic reaction of NS and SF, thereby increasing the compressive strength during the middle stage of hydration. With ongoing industrial development and coal consumption, the importance of research on carbon neutrality in the construction industry has increased. Accordingly, studies have been conducted on a variety of construction materials, and the use of supplementary cementing materials (SCMs), such as fly ash (FA), ground granulated blast-furnace slag (GGBS), and silica fume (SF), has attracted attention ,2,3. SCM2SO4-activated high-volume fly ash (HVFA) concrete leads to higher resistance and less strength loss. Bondar et al. [Of these SCMs, FA has been widely used as a cement admixture because of its low heat of hydration, shrinkage-reducing effect, and high durability. Roshani et al.  predicter et al.  reported2 (NS) and nano-TiO2 [In recent years, there has been growing interest in research to improve the performance of cementitious materials, including the use of nanomaterials to improve the initial strength of HVFA concrete. In general, nanomaterials accelerate the hydration of cement and improve its microstructure, thereby enhancing its mechanical performance and durability ,21,22. Tano-TiO2 ,24,25,26ano-TiO2  used nanNano-silica (NS) is a material with very small particle sizes and the characteristics of a pozzolanic material. The dissolved silica component reacts with the calcium hydroxide generated from the cement hydration reaction to form additional calcium silicate hydrate (C-S-H) and contribute to strength development ,55,56,57As NS may form weak zones in the matrix because of its agglomeration characteristics, NS dispersibility is an important matter of concern . An exceThe purpose of this study was to investigate the effects of NS and SF on the hydration reaction of HVFC. The main focus of this study is to solve the dispersibility problem caused by the agglomeration of NS powder in previous studies. To this end, NS was prepared in the form of a suspension so that the nano-silica particles were not physically agglomerated with each other. In addition, SF having a larger particle size than NS was mixed with NS to prepare an NSF suspension with a multiple particle size distribution. These two solutions were applied to an HVFC paste in which 50% of the cement volume was replaced with FA. The main variables were the NS and SF content. The hydration properties were evaluated through the setting time, heat of hydration, and compressive strength. In addition, the refinement of the pore structure by NS was analyzed using mercury intrusion porosimetry (MIP).2 (50.5%), Al2O3 (24.3%), CaO (9.93%), and Fe2O3 (5.96%), corresponding to class F according to the ASTM C618 specifications. The main component of NS is SiO2 (99.9%). The densities of OPC, FA, SF, and NS are 3.16, 2.32, 2.26, and 2.23 g/cm3, respectively.For this study, ordinary Portland cement (OPC), FA, SF, and NS were used as binders. The chemical compositions of the raw materials were analyzed using an X-ray fluorescence (XRF) spectrometer , with the results as shown in For the study, two types of solution were prepared and used to accelerate the hydration of an FA–cement mixture. The first was an NS solution with a solids content of 5%. The other, called the NSF solution, was prepared by mixing NS powder and SF at a weight ratio of 1:8; it had a solids content of 32%. Cement pastes were prepared using the NS and NSF solutions, as shown in After OPC and FA were placed in a forced mixer and mixed for approximately 30 s, NS or NSF solution was added, and the result was mixed at a speed of approximately 100 rpm for approximately 60 s, and then at approximately 200 rpm for 5 min. The paste specimen was then poured into a mold of size 40 mm × 40 mm × 160 mm and cured in a constant temperature and humidity chamber at a temperature of 20 ± 1 °C and a relative humidity of 90% or higher for one day. It was then demolded and subjected to water curing at 20 ± 1 °C until the test measurement date.The hydration heat flow for each variable was measured using a TAM Air isothermal calorimeter . After a paste was prepared for each variable according to the mix proportions in MIP analysis was conducted using a Micromeritics AutoPore IV 9500  to measure the pore size distribution and cumulative porosity of each specimen. At each measurement age, a sample was collected from the center of the specimen, and hydration was stopped. The sample was then vacuum-dried and stored to prevent contamination by carbonation. The pressure of mercury intrusion varied from 0 to 30,000 psi.The cumulative heat release results b show thAs shown in In addition, NS particles accelerate the cement hydration reaction by providing more nucleation sites for the precipitation of hydration products during the initial hydration process . NS part2), known as the pozzolanic reaction [44− and AlO2− ions as the hydration reaction continues. They react with the Ca2+ ions eluted from the C3S particles to form C-S-H gels around the pozzolan particles. This pozzolanic reaction of NS and SF improves long-term strength during the cement hydration process [NS and SF particles can generate C-S-H gels through fast reactions with calcium hydroxide (Ca(OH)reaction . NS and  process . Hanif e process .For all specimens, pore size tended to decrease as age increased. At 1 day, the main pore size of Plain ranged from 500 to 1000 nm. For the other specimens, however, the peaks in pore size occur in approximately the 300–700 nm range. At 3 and 7 days, the pore sizes of all specimens tended to be less than that at 1 day. This appears to be due to the filler effect of NS and SF particles. At 28 days, pore diameters of the non-Plain specimens corresponding to the peaks were substantially less than those of Plain. In particular, the peak in pore size occurs in approximately the 150–200 nm range for NS15 and NS05SF4; this is substantially smaller than for Plain, whose pore sizes peak in the 300–400 nm range. This appears to be due to the improvement in pore structure caused by the pozzolanic activity of NS and SF particles during the middle stage of hydration. Miao et al.  reported3/g, respectively. Although Plain showed the lowest value, the difference was negligible. At 3 days, however, a different pattern was observed. By 7 days, the difference was pronounced: the cumulative pore volumes of Plain, NS05, NS15, and NS05SF4 were 0.124, 0.121, 0.096, and 0.114 cm3/g, respectively. Plain showed the highest value, and the value of NS15 was 77.8% that of Plain. These results display a pattern similar to that in the compressive strength increase results for the interval from 3 to 7 days of age. Then, at 28 days, the pore volumes of the non-Plain specimens were less than those of Plain, with the pore volume of NS05SF4 having the lowest value (0.084 cm3/g). This again is similar to the compressive strength results and appears to be due to the formation of additional reaction products caused by the additional pozzolanic reaction activation of NS and SF particles that penetrated into the cement composite after 7 days. The ratios of the pore volumes at 28 days compared with 7 days for Plain, NS05, NS15, and NS05SF4 were 81.2%, 81.1%, 96.0%, and 73.3%, respectively. NS15 displayed the lowest reduction in pore volume, and NS05SF4 exhibited the highest. This appears to be due to the effect of the pozzolanic reaction for SF with larger particle sizes.This study investigated the effects of nano-silica (NS) and silica fume (SF) on the hydration reaction of high-volume fly ash cement (HVFC). The main findings of the study are as follows.The initial and final setting times of HVFC were shortened as the NS and SF particle content increased. The setting time was shorter when NS and SF were added together than when NS was used alone. In particular, in the case of the specimen containing the NSF solution with multiple particle sizes, the initial setting time was reduced by 35.5% from that of the plain specimen. This appears to be the result of the NSF solution with multiple size distribution filling the gaps between the cement particles and improving the filling rate.The peak occurrence time of hydration heat flow decreased and the magnitude of the peak increased as the NS content increased. The cumulative heat release of all non-plain specimens was higher than that of the plain specimen until 22 h of hydration. This appears to be because NS and SF particles acted as additional nucleation sites for cement hydrates during the initial stage of hydration. In the case of the specimen containing the NSF solution, however, the cumulative heat release became less than that for NS05 or NS15 after approximately 22 h of hydration. These results are similar to the previous studies that reported that the cumulative caloric value decreased after 1 day of age when NS and SF were added together. Therefore, future studies on optimal NS and SF content are needed.When NS and SF were mixed, the compressive strength of the cement composite showed a tendency to improve. The compressive strengths of all specimens were higher than that of the Plain at all ages, except for the compressive strength of NS05 at 7 days of age. In addition, the increase in the compressive strength of all non-plain specimens after 7 days was greater than that of the Plain, the largest increase being in the age interval from 7 to 28 days. This appears to be because the pozzolanic reaction was accelerated by NS and SF particles. A quantitative study on the effect of pozzolanic reaction of NS and SF on long-term compressive strength is needed in the future.The pore size of the specimens decreased according to NS and SF particle content. After 28 days of age, the pore diameters of the non-plain specimens were substantially less than those of the Plain. This appears to be due to the improvement in pore structure caused by the pozzolanic activity of NS and SF particles during the middle stage of hydration. In addition, the cumulative pore volumes of all specimens decreased after 7 days of age, with the greatest decrease being that at 28 days in the specimen containing the NSF solution.NS and SF improved the early strength of cement composites and improved the internal pore structure. In particular, the mechanical properties and pore structure of the cement composite were further improved when NS and SF were mixed together than when NS was used alone. It seems that the properties of the cement composite will vary depending on the amount of NS and SF mixed. Therefore, in order to apply nanomaterials such as NS and SF to construction products, research on the optimum mix design suitable for the characteristics of each construction product is required in the future.