Interfacial quantum chemical characterization of aromatic organic matter adsorption on oxidized microplastic surfaces.

Examining the adsorption efficiency of individual contaminants on microplastics (MPs) is resource-intensive and time-consuming. To address this challenge, combined laboratory adsorption experiments with model simulations were performed to investigate the adsorption capacities and mechanisms of MPs before and after aging. Our adsorption experiments revealed that aged polyethylene (PE) and polyvinyl chloride (PVC) MPs exhibited increased adsorption capacity for benzene, phenol, and naphthalene. Additionally, density functional theory (DFT) simulations provided insights into changes in adsorption sites, adsorption energy, and charge density on MPs. The π bond of the benzene ring emerged as a pivotal factor in the adsorption process, with van der Waals forces exerting dominant influence. For instance, the adsorption energy of benzene on pristine PE was -0.01879 eV. When oxidized groups, such as hydroxyl, carbonyl, and carboxyl, on the surface of aged PE became the adsorption sites, the adsorption energy increased to -0.06976, -0.04781, and -0.04903 eV, respectively. Regions with unoxidized functional groups also exhibited higher adsorption energies than pristine PE. These results indicated that aged PE had a stronger affinity for benzene compared to pristine PE, enhancing its adsorption. Charge density difference and energy density of states corroborated this observation, revealing larger π-bond charge accumulation areas for benzene on aged PE, suggesting stronger dipole interactions and enhanced adsorption. Similar trends were observed for phenol and naphthalene. In summary, the DFT calculations aligned with the adsorption experiment findings, confirming the effectiveness of simulation methods in predicting changes in the adsorption performance of aged MPs.

Microplastics and volatile organic compounds released from face masks after disinfection: Layers and materials differences.

Effective disinfection methods are critical for ensuring the reusability of masks, yet these methods may inadvertently introduce health concerns associated with microplastics (MPs) and volatile organic compounds (VOCs). This study investigated the impact of ultraviolet germicidal irradiation (UVGI) and sodium hypochlorite (NaClO) bleaching on mask filter layers composed of four distinct materials. Our results revealed that UVGI induced more pronounced damage compared to bleaching, leading to the significant release of both MPs and VOCs. After UVGI treatment at conventional disinfection doses, meltblown (MB) fabrics released MPs reaching 864 ± 182 µg/g (92 ± 19 particles/g). For all filter layers, the quantity of released MPs followed the order: MB > HDPE>PU ≈ NW. These MPs were identified as degraded debris from the mask filter layers. The specific VOCs generated varied depending on the material composition. Non-woven (NW) and MB fabrics, both comprised of polypropylene, predominantly produced various branched aliphatic hydrocarbons and their derivative oxides. The cotton-like fabric, composed of high-density polyethylene, primarily emitted different linear aliphatic hydrocarbons and oxygenates. In contrast, the polyurethane filter layer of reusable masks released aromatic compounds, nitrogenous compounds, and their oxidation products. The formation of VOCs was primarily attributed to bond breakage and oxidative damage to the filter structure resulting from the disinfection process. In summary, as UVGI induced higher yields of MPs and VOCs compared to bleaching, the latter would be a safer option for mask disinfection.

Determination of benzo(a)pyrene in peanut oil based on Raman spectroscopy and machine learning methods.

Benzo(a)pyrene (BaP) generated in the production process of oil is harmful to human severely as a kind of carcinogenic substance. In this study, the qualitative and quantitative detection of BaP concentration in peanut oil was investigated based on Raman spectroscopy combined with machine learning methods. The glass substrates and magnetron sputtered gold substrates for the Raman spectra were compared and the data preprocessing methods of principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) were used to process Raman signal. Back propagation neural network (BPNN), partial least squares regression (PLSR), support vector machine (SVM) and random forest (RF) algorithms were developed to obtain the qualitative and quantitative detection model of BaP concentration in peanut oil. The results showed that the Raman spectra with the glass substrate was more suitable for the BaP detection than magnetron sputtered gold substrates. RF combined with t-SNE could achieve an accuracy of 97.5% in the qualitative detection of BaP concentration levels in model validation experiment, and the correlation coefficient of the prediction set (Rp) in the quantitative detection was 0.9932, the root mean square error (RMSEP) was 0.8323 µg/kg and the bias was 0.1316 µg/kg. It can be concluded that Raman spectroscopy combined with machine learning methods could provide an effective method for the rapid determination of BaP concentration in peanut oil.

Autophagy: A double-edged sword in intervertebral disk degeneration.

Autophagy is a homeostatic mechanism through which intracellular damaged organelles and proteins are degraded and recycled in response to increased metabolic demands or stresses. Although primarily cytoprotective, dysfunction of autophagy is often associated with many degenerative diseases, including intervertebral disc (IVD) degeneration (IDD). As a main contributing factor to low back pain, IDD is the pathological basis for various debilitating spinal diseases. Either higher or lower levels of autophagy are observed in degenerative IVD cells. Despite the precise role of autophagy in disc degeneration that is still controversial, with difference from protection to aggravation, targeting autophagy has shown promise for mitigating disc degeneration. In the current review, we summarize the changes of autophagy in degenerative IVD cells and mainly discuss the relationship between autophagy and IDD. With continued efforts, modulation of the autophagic process could be a potential and attractive therapeutic strategy for degenerative disc disease.

Reactive oxygen species enhance insulin sensitivity.

Chronic reactive oxygen species (ROS) production by mitochondria may contribute to the development of insulin resistance, a primary feature of type 2 diabetes. In recent years it has become apparent that ROS generation in response to physiological stimuli such as insulin may also facilitate signaling by reversibly oxidizing and inhibiting protein tyrosine phosphatases (PTPs). Here we report that mice lacking one of the key enzymes involved in the elimination of physiological ROS, glutathione peroxidase 1 (Gpx1), were protected from high-fat-diet-induced insulin resistance. The increased insulin sensitivity in Gpx1(-/-) mice was attributed to insulin-induced phosphatidylinositol-3-kinase/Akt signaling and glucose uptake in muscle and could be reversed by the antioxidant N-acetylcysteine. Increased insulin signaling correlated with enhanced oxidation of the PTP family member PTEN, which terminates signals generated by phosphatidylinositol-3-kinase. These studies provide causal evidence for the enhancement of insulin signaling by ROS in vivo.