SEARCH · Search PubMed

Results for “Silicon Dioxide”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

5 recordsLinked to original sources

PubMed2026

Unconfined compressive strength prediction for the ordinary Portland cement-steel slag-silica fume ternary system based on response surface methodology.

This research was undertaken to address environmental concerns associated with industrial solid waste and to reduce cement consumption in geotechnical engineering. It specifically investigates the feasibility of using steel slag (SS) and silica fume (SF) as partial substitutes for ordinary Portland cement (OPC) in soil stabilization. The effects of SS, SF, OPC, and initial moisture content on the unconfined compressive strength (UCS) of stabilized soil were investigated through single-factor experiments and response surface methodology (RSM). The results show that SS and SF can synergistically enhance the strength of stabilized soil, although their interaction effect was not statistically significant within the investigated ranges. Compared with soil stabilized solely with OPC, the addition of 18 % SS and 10 % SF reduced OPC consumption by 3 % without compromising strength. Microstructural and compositional analyses further revealed that SS mainly supplied calcium- and silica-bearing components, while SF provided highly reactive silica and micro-filling effects, jointly promoting hydration reactions and improving the compactness of the stabilized soil matrix. As a result, more hydration products were formed in the OPC/SS/SF-stabilized soil than in the OPC-stabilized soil, which contributed to pore filling and strength enhancement. This study provides useful guidance for the sustainable utilization of industrial solid waste and the low-carbon development of soil stabilization materials.

Construction Materials↗

PubMed2026

Biomimetic mesoporous silica nanosphere ameliorate experimental autoimmune uveitis by delivering sCD83.

Autoimmune uveitis (AU) is an autoimmune disease that may lead to blindness, but there are currently no precise targeted therapies for its prevention and treatment. Dendritic cell (DC) is key cell involved in the pathogenesis of AU, and specific regulation of their state can help improve AU. In this work, mesoporous silica nanospheres were loaded with the immunomodulator soluble CD83 (sCD83) and subsequently camouflaged with dendritic cell (DC) membranes to fabricate the nanocarrier DCM@MSN/sCD83 for treating experimental autoimmune uveitis (EAU). Research results show that DCM@MSN/sCD83 effectively alleviated the symptoms of uveitis in EAU, reduced the proportion of CD4+CD25-T cell/CD4+CD25+T cell and the percentage of DC in the eyes and cervical lymph nodes. It also decreased the expression of STING in Müller cell. Furthermore, the efficacy of DCM@MSN/sCD83 was found to be primarily targeting DC, and promoted the expression of IL-10 and TGF-β1 in DC by activating the phosphorylated HIF/STAT3 pathway, to induce the production of CD4+CD25+ T. This effect is superior to nanomedicine loaded with dexamethasone. Moreover，DCM enabled the nanocarriers to efficiently cross the blood-eye barrier and reach cervical lymph nodes, thereby regulating peripheral immunity. This research indicate that cell membrane-modified nanoparticles targeting homologous cells can effectively improve treatment efficiency and duration, which is potential therapy strategy for uveitis.

Animals↗

PubMed2026

Littoral and wetland vegetation decrease carbon emissions from dry inland waters.

Lakes are recognized as active components of the inland water carbon (C) cycle, as organic matter is processed by microbial respiration, inducing large carbon dioxide (CO2) and methane (CH4) emissions. In the context of long-lasting drought periods, large uncertainties remain about: (1) the influence of wet-dry cycle on CO2 and CH4 fluxes in littoral zones and lacustrine wetlands; and (2) the contribution of emergent vegetation to C fluxes in dry inland waters. At the water-land interface of two shallow lakes, this study focuses on CO2 and CH4 fluxes from vegetated and bare dry inland waters in relation to hydrological fluctuations. Three seasonal campaigns were conducted to measure daytime CO2 and CH4 fluxes in pelagic, littoral and wetland surface waters, as well as in temporarily air-exposed sediments, using floating and static chambers, respectively. Our results reveal that wet-dry cycle in the littoral zone and wetlands strongly influence gaseous C fluxes through contrasting patterns, especially in late summer, when the biological processes are most active (primary production and respiration). In air-exposed littoral zones, organic-poor sandy sediments presented the lowest CO2 and CH4 emissions, whereas in air-exposed lacustrine wetlands, water-saturated sediments accumulated high amounts of plant-derived organic matter, promoting intense microbial activity and the highest C emissions. However, amphiphytes and helophytes vegetation in exposed littoral zones and wetlands reversed the direction of C fluxes, inducing the highest CO2 uptake due to high photosynthesis rates. This study underlines the relevance of considering vegetation in dry inland waters, particularly in lacustrine littoral zones and wetlands, to obtain comprehensive lake C budgets, especially under climate change scenarios.

Wetlands↗

PubMed2026

Biomass burning contributions to Mexico City's atmospheric CO2 estimated using a multi-isotope approach.

Fossil fuel combustion dominates anthropogenic emissions worldwide; however, special attention should be addressed to biomass burning, since it is an increasingly important contributor under warmer, drier fire-weather conditions exacerbated by climate change. These emissions could impact the atmospheric composition of heavily urbanized environments. To assess the influence of biomass burning, along with fossil fuels combustion and soil and plant respiration on Mexico City's atmospheric composition, we conducted a year-long (December 2023-December 2024) isotopic monitoring of atmospheric CO2, combining radiocarbon (Δ14C), CO2 stable isotopes (δ13C, δ18O), and CO/CO2 ratios. Once defined the isotopic signatures and the characteristic CO/CO2 that describe the sources, we performed Monte Carlo simulations under two tracer modalities (Δ14C + CO/CO2 and Δ14C + CO2 stable isotopes) for source apportionment. Results show that during the dry season, particularly in March-April, atmospheric Δ14C values approached and overlapped background levels despite Mexico City's fossil-fuel dominance, indicating enhanced non-fossil inputs. Moreover, backward HYSPLIT trajectories supported that these elevated Δ14C values coincided with regional wildfire activity. Monte Carlo results attributed up to ∼34 %-60 % of local CO2 to biomass burning in April, followed by March with contributions of ∼31 %-49 %. Since these biomass burning figures contrast with the official emissions inventory of Mexico City metropolitan area, which assigns <1 % of total CO2 emissions to biomass burning, this study could exhibit the emissions inventory underestimation of this source, and thus, the need to reassess and incorporate top-down isotopic constraints in fire-affected urban regions.

Mexico↗

PubMed2026

Non-destructive prediction of lead content in oilseed rape leaves by fluorescence hyperspectral technology based on neural network.

Based on fluorescence hyperspectral imaging (FHSI), this study targeted rapid, non-destructive quantification of lead (Pb) content in oilseed rape leaves treated with varying silicon (Si) concentrations, acquiring fluorescence spectra over the 484.43-1001.61 nm wavelength range. To optimize spectral data quality, preprocessing methods (Savitzky-Golay smoothing, first derivative, detrending) were comprehensively compared. Characteristic wavelengths were then selected via interval variable iterative shrinkage, which effectively compressed data dimensionality and reduced computational load. A hybrid SE-CL1DA model, fusing a 1D convolutional neural network, a long short-term memory network and SE attention mechanism was constructed, with Bayesian optimization tuning hyperparameters to boost stability. The BO-SE-CL1DA outperformed both traditional machine learning and insufficiently optimized deep learning model (Rp2=0.9609, RMSE = 0.0377 mg/kg, RPD = 5.1736), thus enabling accurate Pb estimation, supporting Si-regulated heavy metal stress management and facilitating agricultural contamination monitoring.

Plant Leaves↗