Nanoscale Heat Transport of Vertically Aligned Carbon Nanotube Bundles for Thermal Management Applications.

Electronic devices continue to shrink in size while increasing in performance, making excess heat dissipation challenging. Traditional thermal interface materials (TIMs) such as thermal grease and pads face limitations in thermal conductivity and stability, particularly as devices scale down. Carbon nanotubes (CNTs) have emerged as promising candidates for TIMs because of their exceptional thermal conductivity and mechanical properties. However, the thermal conductivity of CNT films decreases when integrated into devices due to defects and bundling effects. This study employs a novel cross-sectional approach combining high-vacuum scanning thermal microscopy (SThM) with beam-exit cross-sectional polishing (BEXP) to investigate the nanoscale morphology and thermal properties of vertically aligned CNT bundles at low and room temperatures. Using appropriate thermal transport models, we extracted effective thermal conductivities of the vertically aligned nanotubes and obtained 4 W m-1 K-1 at 200 K and 37 W m-1 K-1 at 300 K. Additionally, non-negligible lateral thermal conductance between CNT bundles suggests more complex heat transfer mechanisms in these structures. These findings provide unique insights into nanoscale thermal transport in CNT bundles, which is crucial for optimizing novel thermal management strategies.

Low Leakage-Current InAsSb Nanowire Photodetectors on Silicon.

Axially doped p-i-n InAs0.93Sb0.07 nanowire arrays have been grown on Si substrates and fabricated into photodetectors for shortwave infrared detection. The devices exhibit a leakage current density around 2 mA/cm(2) and a 20% cutoff of 2.3 µm at 300 K. This record low leakage current density for InAsSb based devices demonstrates the suitability of nanowires for the integration of III-V semiconductors with silicon technology.

Single-molecule in vivo imaging of bacterial respiratory complexes indicates delocalized oxidative phosphorylation.

Chemiosmotic energy coupling through oxidative phosphorylation (OXPHOS) is crucial to life, requiring coordinated enzymes whose membrane organization and dynamics are poorly understood. We quantitatively explore localization, stoichiometry, and dynamics of key OXPHOS complexes, functionally fluorescent protein-tagged, in Escherichia coli using low-angle fluorescence and superresolution microscopy, applying single-molecule analysis and novel nanoscale co-localization measurements. Mobile 100-200nm membrane domains containing tens to hundreds of complexes are indicated. Central to our results is that domains of different functional OXPHOS complexes do not co-localize, but ubiquinone diffusion in the membrane is rapid and long-range, consistent with a mobile carrier shuttling electrons between islands of different complexes. Our results categorically demonstrate that electron transport and proton circuitry in this model bacterium are spatially delocalized over the cell membrane, in stark contrast to mitochondrial bioenergetic supercomplexes. Different organisms use radically different strategies for OXPHOS membrane organization, likely depending on the stability of their environment.

Inferring diffusion in single live cells at the single-molecule level.

The movement of molecules inside living cells is a fundamental feature of biological processes. The ability to both observe and analyse the details of molecular diffusion in vivo at the single-molecule and single-cell level can add significant insight into understanding molecular architectures of diffusing molecules and the nanoscale environment in which the molecules diffuse. The tool of choice for monitoring dynamic molecular localization in live cells is fluorescence microscopy, especially so combining total internal reflection fluorescence with the use of fluorescent protein (FP) reporters in offering exceptional imaging contrast for dynamic processes in the cell membrane under relatively physiological conditions compared with competing single-molecule techniques. There exist several different complex modes of diffusion, and discriminating these from each other is challenging at the molecular level owing to underlying stochastic behaviour. Analysis is traditionally performed using mean square displacements of tracked particles; however, this generally requires more data points than is typical for single FP tracks owing to photophysical instability. Presented here is a novel approach allowing robust Bayesian ranking of diffusion processes to discriminate multiple complex modes probabilistically. It is a computational approach that biologists can use to understand single-molecule features in live cells.