The performance of carbon nanotube (CNT) cables, a contender for copper-wire replacement, is tied to its metallic and semi-conducting-like conductivity responses with temperature; the origin of the semi-conducting-like response however is an underappreciated incongruity in literature. With controlled aspect-ratio and doping-degree, over 61 unique cryogenic experiments including anisotropy and Hall measurements, CNT cable performance is explored at extreme temperatures (65 mK) and magnetic-fields (60 T). A semi-conducting-like conductivity response with temperature becomes temperature-independent approaching absolute-zero, uniquely demonstrating for the first time the necessity of heterogeneous fluctuation induced tunneling; complete de-doping leads to localized hopping, contrasting graphite’s pure metallic-like response. High-field magneto-resistance (including novel +22% longitudinal magneto-resistance near room-temperature) is analyzed with hopping and classical two-band models, both yielding a similar parameter useful for conductor development. Varying field-orientation angle uncovers significant two- and four-fold symmetries that are shown to be from Aharonov-Bohm-like corrections to the curvature-induced bandgap, a first for macroscale CNT fibers. Tight-binding calculations using Green's Function formalism model the largest, coherent transport to-date in commensurate CNT bundles in magnetic-field, revealing non-uniform transmission across bundle cross-sections with doping restoring uniformity; independent of doping, transport in bundle-junction-bundle systems are predominantly from CNTs adjacent to the other bundle—demonstrating that smaller bundles are more efficient for electronic transport. The final impact is predicting the ultimate conductivity of heterogeneous CNT cables using temperature and field-dependent transport, surpassing conductivity of traditional metals.
Successful TiO 2 infiltration into PAN fibers through Vapor Phase Infiltration (VPI) was shown, and they were converted to CFs@TiO 2 via carbonization. Used for photocatalytic dye degradation, proving VPI-derived CFs@TiO 2 as durable photocatalysts.
Chiral graphene nanoribbons (chGNRs) can exhibit symmetry-protected electronic states at their zigzag termini. At half filling, each of these end states hosts a highly localized spin-1/2 magnetic moment. Owing to their topological protection, these spins are promising candidates for future applications in spintronics and quantum information technology. chGNRs can be grown on various metal substrates by on-surface synthesis from suitable precursor molecules. However, on electronegative substrates the end states are found to be fully depleted, while they are fully occupied on electropositive substrates. In both cases, this leads to closed-shell electronic configurations and quenching of the magnetic moments in the end states. Using scanning tunneling and noncontact atomic force microscopy, we show that chGNR synthesis on the rare earth surface alloy GdAu2 enables recovering the magnetic moment of their end states [1], by virtue of its work function matching with graphene. Due to the work function modulation along the GdAu2 moiré superstructure, the nanoribbons can be found either in a diradical neutral ground state or a singly anionic doublet. In addition, the exchange field of the ferromagnetic GdAu2 substrate can stabilize a triplet state in neutral chGNRs. Our results demonstrate a route to local control of pi-radical systems adsorbed on metallic substrates. [1] L. Edens et al., Adv. Mater., e10753 (2025)
Photonic techniques combined with chemometrics offer promising opportunities for next-generation medical in vitro diagnostics. In this work, we evaluate the ability of vibrational spectroscopy to distinguish viral respiratory infections that have progressed to pneumonia. Pneumonia remains a major global health burden and is currently the eighth leading cause of death worldwide. Reliable and differentiated diagnoses are still challenging, as existing methods are time-consuming and require specialized laboratories and expertise. We present a rapid, machine-learning-based in vitro approach for classifying influenza A and seasonal flu and for discriminating between these pathogenic strains. To enhance diagnostic performance, we employ data fusion of complementary Raman and Fourier-transform infrared absorption spectra acquired from microliter-scale droplets of human blood plasma. By integrating spectral information from both modalities, the models capture a broader range of physiological changes and more comprehensively reflect the biochemical profile of the samples, leading to more robust classification. Using generalized linear models, we achieve accuracies of up to 95% in distinguishing healthy controls from influenza A- and seasonal flu-infected samples. The results further highlight specific scenarios in which data fusion yields measurable improvements in predictive power.
In ATomic Layer Deposition (ALD) processes, one-factor-at-a-time tuning remains standard, inflating experimental cost and time, and masking factor interactions. Here, a Taguchi L9 (33) design is employed to map how cycle number (A: 100/250/400), temperature (B: 170/180/190 degrees C), and pulse time (C: 10/15/20 ms) influence phase composition and thickness/roughness of ALD-grown titanium dioxide (TiO2) on Si. Reflectivity was modeled using Parratt recursion with Nevot-Croce roughness in Advanced Material Analysis and Simulation Software (AMASS), and signal-to-noise (S/N) optimization applied the "smaller is better" criterion. Across the matrix, thickness is primarily set by A, rising from 4 to 5 nm at 100 cycles to 16-17 nm at 400 cycles, while the roughness percentage decreases as films thicken (14-20 to 3.7-4.0%); B and C tune the rutile content. Interaction plots revealed B & times; C coupling for rutile content and roughness, while Factor A showed additive effects. Analysis of variance (ANOVA) attributed 99.7 and 97.1% of variance in thickness and roughness to A, and rutile variance mainly to B (41.5%) and C (40.9%). Confirmation experiments validated predictions: A1B3C2 minimized rutile (26.2 vs 25.1% predicted), A1B1C1 minimized thickness (4.20 nm), and A3B1C2 yielded the lowest roughness (3.63%). These results deliver statistically supported process windows for ultrathin, low-roughness, anatase-rich TiO2 and outline a minimal-experiment strategy transferable to other ALD systems for controlling crystallography and film characteristics.
