Aluminum Nanoparticle Production by Acetonitrile-Assisted Milling: Effects of Liquid- vs Vapor-Phase Milling and of Milling Method on Particle Size and Surface Chemistry

Milling aluminum balls together with either vapor- or liquid-phase acetonitrile (ACN) leads to production of nanoparticles by mechanical attrition; however, vapor-phase ACN is far more efficient at inducing size reduction, leading to more, smaller, and more uniform particles. The attrition process is also more efficient than traditional milling of particulate starting material and produces nanoparticles with substantially lower contamination levels. This paper is aimed at better understanding the nature of the size reduction process, the chemistry driving it, and the particles it produces. Mass spectrometry was used to probe gases generated during milling, and a combination of X-ray photoelectron spectroscopy, infrared spectroscopy, dynamic light scattering, helium ion microscopy, scanning electron microscopy, and thermogravimetric analysis/mass spectrometry was used to probe the particles and their surface layer. To provide further insight into the chemistry occurring between ACN and aluminum under milling conditions, high-level ab initio theory was used to calculate the structures and energetics for binding and reactions of ACN and its fragments at different sites on an Al₈₀ model surface

Comparative Performance of Three Machine Learning Models in Predicting Influent Flow Rates and Nutrient Loads at Wastewater Treatment Plants

Accurately predicting influent wastewater quality is vital for the efficient operation and maintenance of wastewater treatment plants (WWTPs). This study evaluated three machine learning (ML) models for predicting influent flow rates and nutrient loads of both industrial and domestic wastewaters in WWTPs. These predictions were based on meteorological data and the population migration patterns. The modelsrandom forest, extra trees, and gradient boosting regressorwere successfully applied to three full-scale WWTPs in Shenzhen, China. All the models demonstrated robust performance in predicting influent flow rate, ammoniacal nitrogen (NH3–N), and total nitrogen (TN). Feature importance analysis revealed that the average precipitation over the past n days and population migration were the most influential factors for predicting influent flow rate. Conversely, human activities have a greater impact on pollutant concentrations. Scenario analyses indicated that precipitation contributed to approximately 5%–10% of the wastewater influent, while groundwater infiltration accounted for around 20%. Overall, this study provides a model framework for forecasting wastewater loads using meteorological and population migration data, setting the groundwork for smart management in WWTPs

Comparative Performance of Three Machine Learning Models in Predicting Influent Flow Rates and Nutrient Loads at Wastewater Treatment Plants

Accurately predicting influent wastewater quality is vital for the efficient operation and maintenance of wastewater treatment plants (WWTPs). This study evaluated three machine learning (ML) models for predicting influent flow rates and nutrient loads of both industrial and domestic wastewaters in WWTPs. These predictions were based on meteorological data and the population migration patterns. The modelsrandom forest, extra trees, and gradient boosting regressorwere successfully applied to three full-scale WWTPs in Shenzhen, China. All the models demonstrated robust performance in predicting influent flow rate, ammoniacal nitrogen (NH3–N), and total nitrogen (TN). Feature importance analysis revealed that the average precipitation over the past n days and population migration were the most influential factors for predicting influent flow rate. Conversely, human activities have a greater impact on pollutant concentrations. Scenario analyses indicated that precipitation contributed to approximately 5%–10% of the wastewater influent, while groundwater infiltration accounted for around 20%. Overall, this study provides a model framework for forecasting wastewater loads using meteorological and population migration data, setting the groundwork for smart management in WWTPs

Synthesis of Nanoparticles from Malleable and Ductile Metals Using Powder-Free, Reactant-Assisted Mechanical Attrition

A reactant-assisted mechanochemical method was used to produce copious nanoparticles from malleable/ductile metals, demonstrated here for aluminum, iron, and copper. The milling media is intentionally degraded via a reactant-accelerated wear process, where the reactant aids particle production by binding to the metal surfaces, enhancing particle production, and reducing the tendency toward mechanochemical (cold) welding. The mechanism is explored by comparing the effects of different types of solvents and solvent mixtures on the amount and type of particles produced. Particles were functionalized with oleic acid to aid in particle size separation, enhance dispersion in hydrocarbon solvents, and protect the particles from oxidation. For aluminum and iron, the result is air-stable particles, but for copper, the suspended particles are found to dissolve when exposed to air. Characterization was performed using electron microscopy, dynamic light scattering, Fourier transform infrared spectroscopy, solid state nuclear magnetic resonance, and X-ray photoelectron spectroscopy. Density functional theory was used to examine the nature of carboxylic acid binding to the aluminum surface, confirming the dominance of bridging bidentate binding

Elevated MicroRNA-31 Expression Regulates Colorectal Cancer Progression by Repressing Its Target Gene SATB2

<div><p>Several studies have brought about increasing evidence to support the hypothesis that miRNAs play a pivotal role in multiple processes of carcinogenesis, including cell growth, apoptosis, differentiation, and metastasis. In this study, we investigated the potential role of miR-31 in colorectal cancer (CRC) aggressiveness and its underlying mechanisms. We found that miR-31 increased in CRC cells originated from metastatic foci and human primary CRC tissues with lymph node metastases. Furthermore, the high-level expression of miR-31 was significantly associated with a more aggressive and poor prognostic phenotype of patients with CRC (<i>p</i> < 0.05). The stable over-expression of miR-31 in CRC cells was sufficient to promote cell proliferation, invasion, and migration <i>in vitro</i>. It facilitated tumor growth and metastasis <i>in vivo</i> too. Further studies showed that miR-31 can directly bind to the 3’untranslated region (3’UTR) of SATB2 mRNA and subsequently repress both the mRNA and protein expressions of SATB2. Ectopic expression of SATB2 by transiently transfected with pCAG-SATB2 vector encoding the entire SATB2 coding sequence could reverse the effects of miR-31 on CRC tumorigenesis and progression. In addition, ectopic over-expression of miR-31 in CRC cells induced epithelial-mesenchymal transition (EMT). Our results illustrated that the up-regulation of miR-31 played an important role in CRC cell proliferation, invasion, and metastasis <i>in vitro</i> and <i>in vivo</i> through direct repressing SATB2, suggesting a potential application of miR-31 in prognosis prediction and therapeutic application in CRC.</p> </div

miR-31 promoted aggressive phenotypes of CRC cells <i>in</i><i>vitro</i>.

<p>(A) Real-time RT-PCR analysis of miR-31 expression level in SW480 and DLD-1 cells after ectopic over-expression of miR-31. (B, C) Effect of miR-31 on cell proliferation was measured by CCK-8 assay. (D) Comparison of colony formation effect of CRC cell lines. (E) Cell-cycle distribution of CRC cells infected with miR-31 was detected by flow cytometry analysis. (F, G) Effects of miR-31 ectopic over-expression on cell motilities and invasiveness were determined using wound-healing (F) and matrigel invasion (G) assays. Data were presented as mean ±SD. The results were reproducible in three independent experiments. * <i>p</i> < 0.05, ** <i>p</i> <0.001.</p

Inhibition of miR-630 led to decreased IR sensitivity.

<p>(A) Inhibition of miR-630 significantly decreased IR-induced inhibition rate (B and C) Inhibition of miR-630 strongly decreased caspase 3 and caspase 6 activities following IR exposure. Error bars represent the mean of three separate determinations ± standard deviation (SD). Asterisk indicates statistically significant changes: * (P < 0.05), ** (P < 0.01).</p

DNA methylation status and transcription factor CREB mediated miR-630 biogenesis.

<p>(A) 5-aza-CdR significantly upregulated miR-630 expression (B) CREB was predicted to bind to the 5′-UTR of miR-630 host gene ARIH1 (C) CREB within the ARIH1 promoter region induced luciferase activity and mutation of predicted CREB binding sites reversed its effect (D) ChIP confirmed the binding of CREB to the ARIH1 promoter. Error bars represent the mean of three separate determinations ± standard deviation (SD). Asterisk indicates statistically significant changes: * (P < 0.05), ** (P < 0.01).</p

miR-630 expression levels reduced after radiation.

<p>(A and B) Endogenous miR-630 expression and IR-induced inhibition rate of serial CRC cell lines (C) miR-630 level decrease after radiation compared with their initial level. Error bars represent the mean of three separate determinations ± standard deviation (SD). Asterisk indicates statistically significant changes: * (P < 0.05), ** (P < 0.01).</p

SATB2 was a direct target of miR-31 in CRC cells.

<p>(A) Schematic illustration of the predicated miR-31-binding sites (S1 and S2) in SATB2 3’-UTR. The mutant binding site was underlined. (B) Luciferase reporter assays in SW480 cells, with cotransfection of wild type or mutated 3’UTR and miRNA as indicated. NS denotes no statistical significance. (C, D) Levels of SATB2 mRNA and protein after miR-31-induced expression in CRC cell lines examined by real-time RT-PCR (C) and western blot (D). Ectopic expression of miR-31 decreased the endogenous levels of SATB2 mRNA and protein. (E) A statistically significant inverse correlation between miR-31 and SATB2 mRNA expression in CRC tissues (Spearman's correlation analysis, <i>r</i> = −0.687;  <i>p</i>=0.001, n = 20).</p