液相色谱-串联质谱技术在临床检验中的应用研究进展

液相色谱-串联质谱（LC-MS/MS）技术具有高灵敏度、高特异性、高分辨率和高效率的优点。近年来随着仪器灵敏度的提高，LC-MS/MS在常规临床检验中显示出极大的潜力，并在疾病早期预防和诊断中发挥着不可替代的作用。本文对LC-MS/MS在新生儿疾病筛查、维生素D检测、内分泌激素检测、肽类和蛋白质定量分析等临床检验方面的研究进展进行综述，并讨论了未来面临的挑战。     

Catalytic behavior in CH4 decomposition of catalysts derived from red mud: Impact of residual Na2O

Catalyst samples for CH₄ decomposition were prepared from red mud (RM) by an acid-leaching neutralization precipitation approach. Water-washing the resultant precipitates multiple times, followed by drying at 105 °C and calcination at 500 °C, resulted in a threshold of residual Na₂O, equivalent to 96% Na₂O removal. Drying the precipitate at a higher temperature of 200 °C, followed by repeated water washing, provided a deeper Na₂O removal of 99% and made the resultant samples more active for the targeted reaction. Subsequently, four catalyst samples with a simulated red mud composition and NaOH contents from 0 to 0.3 wt% were prepared and the catalytic test results revealed that the Na₂O remaining in the RM-derived catalysts did not only inhibit their activation in CH₄ but also lower their maximal activities for CH₄ decomposition. Finally, two catalysts with the same simulated red mud composition and their Na impregnated respectively on Fe₂O₃ and a mixture support of Al₂O₃-SiO₂-TiO₂ were prepared and tested to explore the effect of Na distribution on the activation behavior of RM-derived catalysts for CH₄ decomposition. The activity testing results showed that it was the Na residual dispersed on iron oxides in the RM-derived samples to significantly inhibit the activation of CH₄ decomposition.     

Reusable Polyacrylonitrile-Sulfur Extractor of Heavy Metal Ions from Wastewater

Mercury, lead, and cadmium are among the most toxic and carcinogenic heavy metal ions (HMIs), posing serious threats to the sustainability of aquatic ecosystems and public health. There is an urgent need to remove these ions from water by a cheap but green process. Traditional methods have insufficient removal efficiency and reusability. Structurally robust, large surface-area adsorbents functionalized with high-selectivity affinity to HMIs are attractive filter materials. Here, an adsorbent prepared by vulcanization of polyacrylonitrile (PAN), a nitrogen-rich polymer, is reported, giving rise to PAN-S nanoparticles with cyclic π-conjugated backbone and electronic conductivity. PAN-S can be coated on ultra-robust melamine (ML) foam by simple dipping and drying. In agreement with hard/soft acid/base theory, N- and S-containing soft Lewis bases have strong binding to Hg²⁺, Pb²⁺, Cu²⁺, and Cd²⁺, with extraordinary capture efficiency and performance stability. Furthermore, the used filters, when collected and electrochemically biased in a recycling bath, can release the HMIs into the bath and electrodeposit on the counter-electrode as metallic Hg⁰, Pb⁰, Cu⁰, and Cd⁰, and the PAN-S@ML filter can then be reused at least 6 times as new. The electronically conductive PAN-S@ML filter can be fabricated cheaply and holds promise for scale-up applications.     

Spatiotemporal Magnetocaloric Microenvironment for Guiding the Fate of Biodegradable Polymer Implants

The degradation behavior of implants is significantly important for bone repair. However, it is still unprocurable to spatiotemporally regulate the degradation of the implants to match bone ingrowth. In this paper, a magneto-controlled biodegradation model is established to explore the degradation behavior of magnetic scaffolds in a magnetothermal microenvironment generated by an alternating magnetic field (AMF). The results demonstrate that the scaffolds can be heated by magnetic nanoparticles (NPs) under AMF, which dramatically accelerated scaffold degradation. Especially, magnetic NPs modified by oleic acid with a better interface compatibility exhibit a greater heating efficiency to further facilitate the degradation. Furthermore, the molecular dynamics simulations reveal that the enhanced motion correlation between magnetic NPs and polymer matrix can accelerate the energy transfer. As a proof-of-concept, the feasibility of magneto-controlled degradation for implants is demonstrated, and an optimizing strategy for better heating efficiency of nanomaterials is provided, which may have great instructive significance for clinical medicine.     

Self-assembled cubic-hexagonal perovskite nanocomposite as intermediate-temperature solid oxide fuel cell cathode

Self-assembly is an emerging strategy for preparing composite cathodes with good oxygen electrochemical reduction activity and congenital chemical compatibility for intermediate-temperature solid oxide fuel cell (IT-SOFC). Here we report that a self-assembled BaCo_0.6Zr_0.4O_3-δ (BZC-BC) nanocomposite is prepared through one-pot glycine-nitrate process and exhibits high cathode performance. The BZC-BC nanocomposite is composed of 62 mol% cubic perovskite BaZr_0.82Co_0.18O_3-δ (BZC) as an ionic conductor and 38 mol% hexagonal perovskite BaCo_0.96Zr_0.04O_2.6+δ (12H-BC) as a mixed ionic and electronic conductor. The BZC-BC nanocomposite has the pomegranate-like particles aggregated with a larger number of nanoparticles (50-100 nm) which greatly enlarge the three-phase boundary sites. The BZC-BC nanocomposite exhibits a thermal expansion coefficient of 12.89 × 10⁻⁶ K⁻¹ well-matched with that of Ce_0.8Gd_0.2O_3-δ (12.84 × 10⁻⁶ K⁻¹) electrolyte. The high electro-catalytic activity of BZC-BC nanocomposite cathode for oxygen reduction is reflected by the low polarization resistances of oxygen ions incorporation at cathode/electrolyte interface (0.02823 Ω cm²), oxygen species diffusion (0.03702 Ω cm²) and oxygen adsorptive dissociation (0.07609 Ω cm²) at 700 °C. The single cell with BZC-BC nanocomposite cathode achieves the maximum power density of 1094 mW cm⁻² at 650 °C and shows good stability under 25 h run.     

Low shear rheological behaviour of two-phase mesophase pitch

The low shear rate rheology of two phase mesophase pitches derived from coal tar pitch has been investigated. Particulate quinoline insolubles (QI) stabilised the mesophase spheres against coalescence. Viscosity measurements over the range 10–10⁶ Pa s were made at appropriate temperature ranges. Increasing shear thinning behaviour was evident with increasing mesophase content. At low mesophase contents the dominant effect on the near Newtonian viscosity was temperature but at higher contents it was the shear rate; temperature dependence declined to near zero. The data indicated that agglomeration could be occurring at intermediate mesophase volume fractions, 0.2–0.3. The Krieger–Dougherty function and its emulsion analogue indicated that in this region the mesophase pitch emulsions actually behaved like ‘hard’ sphere systems and the effective volume fraction was estimated as a function of shear rate illustrating the change in extent of agglomeration. At the higher volume fractions approaching the maximum packing fraction, which could only be measured at higher temperatures, the shear thinning behaviour changed in character and it is considered that this is possibly due to shear induced deformation and breakup of dispersed drops in the shear field.     

Output-relevant Variational autoencoder for Just-in-time soft sensor modeling with missing data

Main challenges for developing data-based models lie in the existence of high-dimensional and possibly missing observations that exist in stored data from industry process. Variational autoencoder (VAE) as one of the deep learning methods has been applied for extracting useful information or features from high-dimensional dataset. Considering that existing VAE is unsupervised, an output-relevant VAE is proposed for extracting output-relevant features in this work. By using correlation between process variables, different weight is correspondingly assigned to each input variable. With symmetric Kullback–Leibler (SKL) divergence, the similarity is evaluated between the stored samples and a query sample. According to the values of the SKL divergence, data relevant for modeling are selected. Subsequently, Gaussian process regression (GPR) is utilized to establish a model between the input and the corresponding output at the query sample. In addition, owing to the common existence of missing data in output data set, the parameters and missing data in the GPR are estimated simultaneously. A practical debutanizer industrial process is utilized to illustrate the effectiveness of the proposed method.