Mixed-Metal MOF-74 Templated Catalysts for Efficient Carbon Dioxide Capture and Methanation

The vast chemical and structural tunability of metal–organic frameworks (MOFs) are beginning to be harnessed as functional supports for catalytic nanoparticles spanning a range of applications. However, a lack of straightforward methods for producing nanoparticle-encapsulated MOFs as efficient heterogeneous catalysts limits their usage. Herein, a mixed-metal MOF, NiMg-MOF-74, is utilized as a template to disperse small Ni nanoclusters throughout the parent MOF. By exploiting the difference in Ni O and Mg O coordination bond strength, Ni²⁺ is selectively reduced to form highly dispersed Ni nanoclusters constrained by the parent MOF pore diameter, while Mg²⁺ remains coordinated in the framework. By varying the ratio of Ni to Mg in the parent MOF, accessible surface area and crystallinity can be tuned upon thermal treatment, influencing CO₂ adsorption capacity and hydrogenation selectivity. The resulting Ni nanoclusters prove to be an active catalyst for CO₂ methanation and are examined using extended X-ray absorption fine structure and X-ray photoelectron spectroscopy. By preserving a segment of the Mg²⁺-containing MOF framework, the composite system retains a portion of its CO₂ adsorption capacity while continuing to deliver catalytic activity. The approach is thus critical for designing materials that can bridge the gap between carbon capture and CO₂ utilization.     

Sintering behaviour of fluorapatite–silicate composites produced from natural fluorapatite and quartz

In this work, the sintering behaviour of fluorapatite (FAp)–silicate composites prepared by mixing variable amounts of natural quartz (2.5 wt% to 20 wt%) and FAp was studied. The composites were pressureless sintered in air at temperatures from 1000 °C to 1350 °C. The effects of temperatures on the densification, phase formation, chemical bonding and Vickers hardness of the composites were evaluated. All the samples exhibited mixed phase, comprising FAp and francolite as the major constituents along with some minor phases of cristobalite, wollastonite, dicalcium silicate and/or whitlockite dependent on the quartz content and sintering temperature. The composite containing 2.5 wt% quartz exhibited the best sintering properties. The highest bulk density of 3 g/cm³ and a Vickers hardness of >4.2 GPa were obtained for the 2.5 wt% quartz–FAp composite when sintered at 1100 °C. The addition of quartz was found to alter the microstructure of the composites, where it exhibited a rod-like morphology when sintered at 1000 °C and a regular rounded grain structure when sintered at 1350 °C. A wetted grain surface was observed for composites containing high quartz content and was believed to be associated with a transient liquid phase sintering.     

Marriage,Social Networks,and Health at Older Ages

Social relationships in older adulthood have strong connections to health and wellbeing. Connections with social network members and with spouses and long-term partners in particular, have an especially important impact on health. We highlight recent research from the National Social Life, Health, and Aging Project (NSHAP), a nationally representative, longitudinal study of aging in America, to describe the different ways health is produced in social contexts. We first discuss how social network characteristics and marital relationships influence health outcomes and sexuality, and then move on to recent findings about the ways health shapes an individual’s social world. We find that features of a social network, apart from simply its size, have strong effects on health behaviors, and that changes in network composition are associated with changes in health. We show that marriage is uniquely protective against damaging biological processes, and that the quality of a marriage influences health and well-being in nuanced ways. Finally we find that health status is associated with the ability to manage one's social network, and that health status affects the quality of one's marriage. We focus our discussion on new data from Wave 2 that enables researchers to explore how changes in social networks and partner relationships affect health and well-being during the last third of life.     

Polychromic carbon black: Laser galvanized multicolour fluorescence display

Recovered carbon black (rCB), a very economical and abundance source of material, is transformed into dazzling multicolour fluorescence and visual display for the first time by way of a scanning focused laser treatment. This laser-initiated process is both straightforward and versatile, catering to both micro- and macro-scopic patterning with the sample in ambient or helium environment. The observed phenomenon is attributed to both chemical and structural induced colouration of rCB powder. Chemically, carbon infusion of oxidised metal occurs when photothermal reaction takes place in ambient. After laser modification with the sample in helium environment, the powder not only fluoresces due to sulphur impurities, control annealing of these powders results in formation of periodic arrangements of carbon nanoparticles. The periodicity of these arrangement falls within the range of visible wavelength, hence contributing to the visually observable rainbow coloured rCB flakes. The patterned sample is also transferrable using PDMS stamps. This in turn broadens the application of this material in flexible electronic devices/displays. Photocurrent measurements show most significant enhancement under yellow light illumination. Furthermore, in the presence of an applied potential, the fluorescence detected from the sample can easily be switched off. All in all, we present a simple process to add multiple functionalities to a material that is both inexpensive and sustainable.

     

Metabolomic Profiling of Plasma from Melioidosis Patients Using UHPLC-QTOF MS Reveals Novel Biomarkers for Diagnosis

To identify potential biomarkers for improving diagnosis of melioidosis, we compared plasma metabolome profiles of melioidosis patients compared to patients with other bacteremia and controls without active infection, using ultra-high-performance liquid chromatography-electrospray ionization-quadruple time-of-flight mass spectrometry. Principal component analysis (PCA) showed that the metabolomic profiles of melioidosis patients are distinguishable from bacteremia patients and controls. Using multivariate and univariate analysis, 12 significant metabolites from four lipid classes, acylcarnitine (n = 6), lysophosphatidylethanolamine (LysoPE) (n = 3), sphingomyelins (SM) (n = 2) and phosphatidylcholine (PC) (n = 1), with significantly higher levels in melioidosis patients than bacteremia patients and controls, were identified. Ten of the 12 metabolites showed area-under-receiver operating characteristic curve (AUC) >0.80 when compared both between melioidosis and bacteremia patients, and between melioidosis patients and controls. SM(d18:2/16:0) possessed the largest AUC when compared, both between melioidosis and bacteremia patients (AUC 0.998, sensitivity 100% and specificity 91.7%), and between melioidosis patients and controls (AUC 1.000, sensitivity 96.7% and specificity 100%). Our results indicate that metabolome profiling might serve as a promising approach for diagnosis of melioidosis using patient plasma, with SM(d18:2/16:0) representing a potential biomarker. Since the 12 metabolites were related to various pathways for energy and lipid metabolism, further studies may reveal their possible role in the pathogenesis and host response in melioidosis.     

Encrypted image classification based on multilayer extreme learning machine

Nowadays, numerous corporations (such as Google, Baidu, etc.) require an efficient and effective search algorithm to crawl out the images with queried objects from databases. Moreover, privacy protection is a significant issue such that confidential images must be encrypted in corporations. Nevertheless, decrypting and then classifying millions of encrypted images becomes a heavy burden to computation. In this paper, we proposed an encrypted image classification framework based on multi-layer extreme learning machine that is able to directly classify encrypted images without decryption. Experiments were conducted on popular handwritten digits and letters databases. Results demonstrate that the proposed framework is secure, efficient and accurate for classifying encrypted images.