A High-Performance Design for Hierarchical Parallelism in the QMCPACK Monte Carlo code

We introduce a new high-performance design for parallelism within the Quantum Monte Carlo code QMCPACK. We demonstrate that the new design is better able to exploit the hierarchical parallelism of heterogeneous architectures compared to the previous GPU implementation. The new version is able to achieve higher GPU occupancy via the new concept of crowds of Monte Carlo walkers, and by enabling more host CPU threads to effectively offload to the GPU. The higher performance is expected to be achieved independent of the underlying hardware, significantly improving developer productivity and reducing code maintenance costs. Scientific productivity is also improved with full support for fallback to CPU execution when GPU implementations are not available or CPU execution is more optimal

Generic epitaxial graphene biosensors for ultrasensitive detection of cancer risk biomarker

A generic electrochemical method of 'bioreceptor' antibody attachment to phenyl amine functionalized graphitic surfaces is demonstrated. Micro-channels of chemically modified multi-layer epitaxial graphene (MLEG) have been used to provide a repeatable and reliable response to nano-molar (nM) concentrations of the cancer risk (oxidative stress) biomarker 8-hydroxydeoxyguanosine (8-OHdG). X-ray photoelectron spectroscopy, Raman spectroscopy are used to characterize the functionalized MLEG. Confocal fluorescence microscopy using fluorescent-labelled antibodies indicates that the anti-8-OHdG antibody selectively binds to the phenyl amine-functionalized MLEG's channel. Current–voltage measurements on functionalized channels showed repeatable current responses from antibody–biomarker binding events. This technique is scalable, reliable, and capable of providing a rapid, quantitative, label-free assessment of biomarkers at nano-molar (<20 nM) concentrations in analyte solutions. The sensitivity of the sensor device was investigated using varying concentrations of 8-OHdG, with changes in the sensor's channel resistance observed upon exposure to 8-OHdG. Detection of 8-OHdG concentrations as low as 0.1 ng ml−1 (0.35 nM) has been demonstrated. This is five times more sensitive than reported enzyme linked immunosorbent assay tests (0.5 ng ml−1)

Probing Surface Chemistry at an Atomic Level; Decomposition of 1-Propanethiol on GaP(001)(2×4) Investigated by STM, XPS, and DFT

The adsorption and decomposition mechanisms for 1-propanethiol on a Ga-rich GaP(001) (2 × 4) surface are investigated at an atomic level using scanning tunneling microscopy (STM), X-ray photoelectron spectroscopy, and density functional theory (DFT) calculations. Using a combination of experimental and theoretical tools, we probe the detailed structures and energetics of a series of reaction intermediates in the thermal decomposition pathway from 130 to 773 K. At 130 K, the propanethiolate adsorbates are observed at the edge gallium sites, with the thiolate–Ga bonding configuration maintained up to 473 K. Further decomposition produces two new surface features, Ga–S–Ga and P-propyl species at 573 K. Finally, S-induced (1 × 1) and (2 × 1) reconstructions are observed at 673–773 K, which are reportedly associated with arrays of surface Ga–S–Ga bonds and subsurface diffusion of S. To understand the observed site-selectivity on the hydrogen dissociation of the thiol molecule at 130 K, the two most likely dissociation pathways (Ga–P vs Ga–Ga dimer sites) are investigated using DFT Gibbs energy calculations. While the theory predicts the kinetic advantage for the dissociation reaction occurring on the Ga–P dimer (Lewis acid–base combination), we only observed dissociation products on the Ga–Ga dimer (Lewis acid). The DFT calculations clarify that the reversible thiolate diffusion along the Ga dimer row prevents recombinative desorption, which is probable on the Ga–P dimer. Together with experimental and theoretical results, we suggest a thermal decomposition mechanism for the thiol molecule with atomic-level structural details