Influence of Oxygen Uptake on Pitch Carbon Fiber.

Carbon fiber precursor materials, such as polyacrylonitrile, pitch, and cellulose/rayon, require thermal stabilization to maintain structural integrity during conversion into carbon fiber. Thermal stabilization mitigates undesirable decomposition and liquification of the fibers during the carbonization process. Generally, the thermal stabilization of mesophase pitch consists of the attachment of oxygen-containing functional groups onto the polymeric structure. In this study, the oxidation of mesophase pitch precursor fibers at various weight percentage increases (1, 3.5, 5, 7.5 wt%) and temperatures (260, 280, 290 °C) using in situ differential scanning calorimetry and thermogravimetric analysis is investigated. The results are analyzed to determine the effect of temperature and weight percentage increase on the stabilization process of the fibers, and the fibers are subsequently carbonized and tested for tensile mechanical performance. The findings provide insight into the relationship between stabilization conditions, fiber microstructure, and mechanical properties of the resulting carbon fibers.

Applying support-vector machine learning algorithms toward predicting host-guest interactions with cucurbit[7]uril.

Machine learning is a valuable tool in the development of chemical technologies but its applications into supramolecular chemistry have been limited. Here, the utility of kernel-based support vector machine learning using density functional theory calculations as training data is evaluated when used to predict equilibrium binding coefficients of small molecules with cucurbit[7]uril (CB[7]). We find that utilising SVMs may confer some predictive ability. This algorithm was then used to predict the binding of drugs TAK-580 and selumetinib. The algorithm did predict strong binding for TAK-580 and poor binding for selumetinib, and these results were experimentally validated. It was discovered that the larger homologue cucurbit[8]uril (CB[8]) is partial to selumetinib, suggesting an opportunity for tunable release by introducing different concentrations of CB[7] or CB[8] into a hydrogel depot. We qualitatively demonstrated that these drugs may have utility in combination against gliomas. Finally, mass transfer simulations show CB[7] can independently tune the release of TAK-580 without affecting selumetinib. This work gives specific evidence that a machine learning approach to recognition of small molecules by macrocycles has merit and reinforces the view that machine learning may prove valuable in the development of drug delivery systems and supramolecular chemistry more broadly.

Hyperbolic graph embedding of MEG brain networks to study brain alterations in individuals with subjective cognitive decline.

An expansive area of research focuses on discerning patterns of alterations in functional brain networks from the early stages of Alzheimer's disease, even at the subjective cognitive decline (SCD) stage. Here, we developed a novel hyperbolic MEG brain network embedding framework for transforming high-dimensional complex MEG brain networks into lower-dimensional hyperbolic representations. Using this model, we computed hyperbolic embeddings of the MEG brain networks of two distinct participant groups: individuals with SCD and healthy controls. We demonstrated that these embeddings preserve both local and global geometric information, presenting reduced distortion compared to rival models, even when brain networks are mapped into low-dimensional spaces. In addition, our findings showed that the hyperbolic embeddings encompass unique SCD-related information that improves the discriminatory power above and beyond that of connectivity features alone. Notably, we introduced a unique metric-the radius of the node embeddings-which effectively proxies the hierarchical organization of the brain. Using this metric, we identified subtle hierarchy organizational differences between the two participant groups, suggesting increased hierarchy in the dorsal attention, frontoparietal, and ventral attention subnetworks among the SCD group. Last, we assessed the correlation between these hierarchical variations and cognitive assessment scores, revealing associations with diminished performance across multiple cognitive evaluations in the SCD group. Overall, this study presents the first evaluation of hyperbolic embeddings of MEG brain networks, offering novel insights into brain organization, cognitive decline, and potential diagnostic avenues of Alzheimer's disease.

Hyperbolic graph embedding of MEG brain networks to study brain alterations in individuals with subjective cognitive decline.

An expansive area of research focuses on discerning patterns of alterations in functional brain networks from the early stages of Alzheimer's disease, even at the subjective cognitive decline (SCD) stage. Here, we developed a novel hyperbolic MEG brain network embedding framework for transforming high-dimensional complex MEG brain networks into lower-dimensional hyperbolic representations. Using this model, we computed hyperbolic embeddings of the MEG brain networks of two distinct participant groups: individuals with SCD and healthy controls. We demonstrated that these embeddings preserve both local and global geometric information, presenting reduced distortion compared to rival models, even when brain networks are mapped into low-dimensional spaces. In addition, our findings showed that the hyperbolic embeddings encompass unique SCD-related information that improves the discriminatory power above and beyond that of connectivity features alone. Notably, we introduced a unique metric-the radius of the node embeddings-which effectively proxies the hierarchical organization of the brain. Using this metric, we identified subtle hierarchy organizational differences between the two participant groups, suggesting increased hierarchy in the dorsal attention, frontoparietal, and ventral attention subnetworks among the SCD group. Last, we assessed the correlation between these hierarchical variations and cognitive assessment scores, revealing associations with diminished performance across multiple cognitive evaluations in the SCD group. Overall, this study presents the first evaluation of hyperbolic embeddings of MEG brain networks, offering novel insights into brain organization, cognitive decline, and potential diagnostic avenues of Alzheimer's disease.

Growth and Mechanics of Heterogeneous, 3D Carbon Nanotube Forest Microstructures Formed by Sequential Selective-Area Synthesis.

Three-dimensional carbon nanotube (CNT) forest microstructures are synthesized using sequenced, site-specific synthesis techniques. Thin-film layers of Al2O3 and Al2O3/Fe are patterned to support film-catalyst and floating-catalyst chemical vapor deposition (CVD) in specific areas. Al2O3 regions support only floating-catalyst CVD, whereas regions of layered Al2O3/Fe support both film- and floating-catalyst CNT growth. Sequenced application of the two CVD methods produced heterogeneous 3D CNT forest microstructures, including regions of only film-catalyst CNTs, only floating-catalyst CNTs, and vertically stacked layers of each. The compressive mechanical behavior of the heterogeneous CNT forests was evaluated, with the stacked layers exhibiting two distinct buckling plateaus. Finite element simulation of the stacked layers demonstrated that the relatively soft film-catalyst CNT forests were nearly fully buckled prior to large-scale deformation of the bottom floating-catalyst CNT forests.