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Aims: To describe the development and report the first‐stage validation of a digital version of the digit symbol substitution test (DSST), for assessment of cognitive function in older people with diabetes. Materials and Methods: A multidisciplinary team of experts was convened to conceptualize and build a digital version of the DSST and develop a machine‐learning (ML) algorithm to analyse the inputs. One hundred individuals with type 2 diabetes (aged ≥ 60 years) were invited to participate in a one‐time meeting in which both the digital and the pencil‐and‐paper (P&P) versions of the DSST were administered. Information pertaining to demographics, laboratory measurements, and diabetes indices was collected. The correlation between the digital and P&P versions of the test was determined. Additionally, as part of the validation process, the performance of the digital version in people with and without known risk factors for cognitive impairment was analysed. Results: The ML model yielded an overall accuracy of 89.1%. A strong correlation was found between the P&P and digital versions (r = 0.76, p < 0.001) of the DSST, as well as between the ML model and the manual reading of the digital DSST (r = 0.99, p < 0.001). Conclusions: This study describes the development of and provides first‐stage validation data for a newly developed digital cognitive assessment tool that may be used for screening and surveillance of cognitive function in older people with diabetes. More studies are needed to further validate this tool, especially when self‐administered and in different clinical settings. [ABSTRACT FROM AUTHOR]

To probe reaction kinetics and polymer decomposition, energetic filaments composed of aluminum (Al) and poly(vinylidene fluoride) (PVDF) with varying hexafluoropropylene (HFP) content are tested for processability and combustion characteristics. Rheological and crystallization findings indicate that the Al‐binder interfacial interactions are disrupted by HFP content. Thermal analysis shows that fuel consumption scales with Al particle size due to the diffusion‐driven Al‐fluoropolymer reaction regardless of HFP concentration. Char yield analysis shows that more solid product is retained in samples with smaller particle diameters, which further reflects the diffusive nature of both Al‐PVDF and Al‐P(VDF‐HFP) reactions. Burn rates reveal two competing mechanisms for reaction efficiency: 1) accelerated binder decomposition through Al‐PVDF interactions and 2) more energetic fluorination due to higher fluorine content in the P(VDF‐HFP) binders. Finally, powder X‐ray diffraction (PXRD) patterns show that AlF3 is the primary product from self‐propagating burns. However, in larger Al particle sizes, filaments are unable to burn completely and result in high levels of Al2O3 and Al4C3 formation, which indicates that these binders are not amenable with low surface area, metallic fuels. These findings aim to improve fluorinated feedstock selection for potential binder candidates in energetic additive manufacturing (AM). [ABSTRACT FROM AUTHOR]

Polyhedral oligomeric silsesquioxane (POSS) nanostructured chemicals, when incorporated at low levels in thermoplastics, provide processability enhancement and viscosity reduction without compromising other bulk physical properties. POSS has been relatively unexplored in high performance polymers, and there is incomplete understanding of the mechanisms by which POSS produces flow improvements. In this study, polyethersulfone (PES) was melt‐blended with trisilanolphenyl (TSP)‐POSS and dodecaphenyl (DP)‐POSS; and rheological, dielectric spectroscopy, and scanning electron microscopy evaluations were conducted to identify structure/property/processing relationships. TSP‐POSS yielded greater processability improvements and viscosity reductions than DP‐POSS, suppressed low temperature relaxations to a larger extent, and displayed a greater degree of nanoscale dispersion in the polymer matrix. The findings are evaluated in terms of competing theories of POSS viscosity reduction. [ABSTRACT FROM AUTHOR]