金斯敦大学

Black phosphorus (BP) is a promising solid lubricant with excellent shear properties, chemical inertness, and high thermal stability. Previous tribological studies have focused exclusively on BP produced via chemical vapor transport (CVT), a method that is costly and yields only small quantities. To finally advance BP from a laboratory curiosity to a practical solid lubricant for real-world tribological applications, this study investigates the tribological performance of BP synthesized through affordable, scalable, and sustainable routes. BP produced from inexpensive and commercially available red phosphorus via solvothermal conversion and mechanochemical methods (ball milling) is investigated in detail. The crystallinity of the resulting materials depends strongly on the synthesis route: in contrast to highly crystalline CVT-BP, BP produced solvothermally or mechanochemically is significantly more amorphous. Both, the synthesized BP samples and tribofilms on steel discs were characterized via XRD, Raman, SEM, EDX and XPS.Tribological tests were conducted using a nanotribometer operating in a linear-reciprocating ball-on-disc configuration. The solvothermally synthesized BP performs poorly as a solid lubricant due to rapid coating removal and pronounced oxidation. However, mechanochemically produced BP exhibits stable, low friction (COF ≈ 0.2), which is fully comparable to CVT-BP. Si₃N₄ and steel counterbodies were employed to assess the counterbody dependence. Our findings show that steel promotes strong BP adhesion and transfer of layers to the counterbody, resulting in higher friction for amorphous samples. In contrast, Si₃N₄ shows weak BP adhesion and delivers consistently low COF for all high-quality BP coatings. These results are supported with first-principles adhesion simulations, which show that BP adhesion is strongest on Fe, weaker on O-passivated Fe and weakest on Si₃N₄.

This study examines the relationship between Artificial Intelligence (AI), supply chain visibility, supplier integration, and organizational performance through the lens of Organizational Information Processing Theory (OIPT). Drawing on empirical data from UK-based organizations that have adopted AI technologies, we employ structural equation modeling along with mediation and moderation analyses to test our hypotheses. The findings indicate that AI does not have a significant direct effect on performance; instead, its impact is fully mediated through supply chain visibility and supplier integration. This supports the view that AI-driven supply chain visibility acts as an information-processing mechanism that enhances firms' ability to coordinate, collaborate, and integrate with supply chain partners, thereby improving organizational performance. Furthermore, supply uncertainty positively moderates the relationship between supplier integration and performance, amplifying the benefits under conditions of higher uncertainty. This study contributes to the literature by clarifying AI's indirect role in driving performance and highlighting the importance of embedding AI within broader organizational capabilities. It also offers practical insights for managers seeking to leverage AI to enhance supply chain integration and organizational effectiveness.

P-cresol, indole and indole-3-acetic acid (IAA) are catabolites of amino acids, formed by the gut microbiome. Most of these aromatic hydrocarbon derivatives are excreted by the colon before reentering the body to form "exogenous" protein-bound uremic toxins (PBUTs), which aggravate chronic kidney disease (CKD). Removal efficiencies of these PBUT precursors from model phosphate-buffered saline solutions by three different surface-modified nanoporous carbon adsorbents (PCs) were studied. PCs were produced by physicochemical and/or acid base activation of carbonized rice husk waste. Removal rates achieved values of 32-96% within a 3 h contact time. High micro/mesoporosity and surface chemistry of the N- and P-doped biochars were established by N2 adsorption studies, SEM/EDS analysis, XPS and FT-IR-spectroscopy. The ammoxidized PC-N1 had the highest adsorption capacity (1.97 mmol/g for IAA, 2.43 mmol/g for p-cresol and 2.42 mmol/g for indole), followed by "urea-nitrified" PC-N2, whilst the phosphorylated PC-P demonstrated the lowest adsorption capacity for these solutes. These results do not correlate with the total pore volume values for PC-N2 (0.91 cm3/g) < PC-P (1.56 cm3/g) < PC-N1 (1.84 cm3/g), suggesting that other parameters such as the micropore volume (PC-N1 > PC-N2 > PC-P) and the interaction of surface chemical functional groups with the solutes play key roles in the adsorption mechanism. N-doped PC-N1 and PC-N2 have basic functional groups with higher affinity with acidic IAA and p-cresol. The ion-exchange mechanism of phenolic and indolic compound chemisorption by nanoporous carbon adsorbents, modified with surface N- and P-containing functional groups, has been proposed.

This paper uses a large individual-record-level dataset on sick leave to examine adult morbidity in the United Kingdom between 1850 and 1908. From 1859 onwards postal workers were eligible to receive a pension or gratuity when they retired or were forced to stop working due to ill health. Their pension application forms contain information on up to 10 years of sick leave taken prior to retirement by each retiree. These data have several advantages over previous sources of information on morbidity in this period. Sick leave was closely monitored by the Post Office's medical service, mitigating issues around the inflation of morbidity raised in the context of friendly society data; they cover the entirety of the United Kingdom; they include workers from the service sector as well as manufacturing occupations; and they include a greater proportion and number of women than previous sources. The paper discusses the nature of these data and the administrative systems that created them before examining the trends and modelling the determinants of sick leave. It concludes that postal workers became increasingly likely to take time off during this period but that the duration of sick leave remained stable. These changes were driven by changes to sick leave regulations; shifts in the composition of the workforce by age, occupation, gender, and location; and changes in the intensity of postal work.

This study presents the multidimensional style anchoring-dynamic feedback calibration (MSA-DFC) algorithm, designed to mitigate style drift and enhance consistency in generative artificial intelligence (AIGC) outputs across diverse generation modalities and long-sequence tasks. The algorithm constructs a text-visual multidimensional style primitive model that integrates semantic mood vectors, syntactic structure matrices, color distributions, and composition rules. These features are mapped into a unified cross-modal representation space through a modality adaptation network. A domain-adaptive style anchoring mechanism improves style adaptation accuracy, while a dynamic feedback calibration module suppresses style drift during long-sequence generation. To evaluate the algorithm, a hybrid dataset combining expanded public resources and self-constructed multi-scenario data was developed, along with a dual-dimensional evaluation system incorporating objective performance and subjective experience metrics. Experimental results show that MSA-DFC improves style similarity by 29.4% and reduces the style drift rate to as low as 3.2% compared to baseline methods. The algorithm also achieves a user satisfaction score of 88.7 and reduces task completion time by 27.4%, with all improvements being statistically significant (p<0.001). The proposed method outperforms mainstream models such as StyleGAN3 and ChatGLM-6B (fine-tuned) across multiple scenarios. This work addresses the core challenge of style control in AIGC and establishes a quantitative correlation between style consistency and user experience, providing both technical and theoretical support for the practical deployment of AIGC systems.