A facile hydrothermal synthesis route to N and S, N co-doped graphene quantum dots (GQDs) was developed by using citric acid as the C source and urea or thiourea as N and S sources. Both N and S, N doped GQDs showed high quantum yield (78% and 71%), excitation independent under excitation of 340-400 nm and single exponential decay under UV excitation. A broad absorption band in the visible region appeared in S, N co-doped GQDs due to doping with sulfur, which alters the surface state of GQDs. However, S, N co-doped GQDs show different color emission under excitation of 420-520 nm due to their absorption in the visible region. The excellent photocatalytic performance of the S, N co-doped GQD/TiO2 composites was demonstrated by degradation of rhodamine B under visible light. The apparent rate of S, N:GQD/TiO2 is 3 and 10 times higher than that of N:GQD/TiO2 and P25 TiO2 under visible light irradiation, respectively.

Chromium(VI) [Cr(VI)] is considered as a severe environmental pollutant, due to its highly toxic and carcinogenic properties. Therefore, low cost, highly sensitive sensors for the determination of Cr(VI) are highly demanded. It is well-known that highly luminescent carbon dots (CDs) have been successfully applied as fluorescent nanosensors for pH, ions, and molecular substances. In the present work, we have demonstrated an on-off fluorescent CD probe for detecting Cr(VI) based on the inner filter effect (IFE) because the absorption bands of Cr(IV) fully covered the emission and excitation bands of CDs. This CD-based nanosensor provides obvious advantages of simplicity, convenience, rapid response, high selectivity, and sensitivity, which have potential application for the detection of Cr(VI) in the environmental industry. In addition, because Cr(VI) can be reduced to low valent chromium species easily by reductant, resulting in the elimination of the IFE and recovery of CD fluorescence, the CD-Cr(VI) mixture could behave as an off-on type fluorescent probe for reductant. We employed ascorbic acid (AA) as an example molecule to demonstrate this off-on type fluorescent probe.

Ion transport in crystalline fast ionic conductors is a complex physical phenomenon. Certain ionic species (e.g., Ag+, Cu+, Li+, F–, O2–, H+) in a solid crystalline framework can move as fast as in liquids. This property, although only observed in a limited number of materials, is a key enabler for a broad range of technologies, including batteries, fuel cells, and sensors. However, the mechanisms of ion transport in the crystal lattice of fast ionic conductors are still not fully understood despite the substantial progress achieved in the last 40 years, partly because of the wide range of length and time scales involved in the complex migration processes of ions in solids. Without a comprehensive understanding of these ion transport mechanisms, the rational design of new fast ionic conductors is not possible. In this review, we cover classical and emerging characterization techniques (both experimental and computational) that can be used to investigate ion transport processes in bulk crystalline inorganic materials which exhibit predominant ion conduction (i.e., negligible electronic conductivity) with a primary focus on literature published after 2000 and critically assess their strengths and limitations. Together with an overview of recent understanding, we highlight the need for a combined experimental and computational approach to study ion transport in solids of desired time and length scales and for precise measurements of physical parameters related to ion transport.

In this work, we evaluate OpenCL as a programming tool for developing performance-portable applications for GPGPU. While the Khronos group developed OpenCL with programming portability in mind, performance is not necessarily portable. OpenCL has required performance-impacting initializations that do not exist in other languages such as CUDA. Understanding these implications allows us to provide a single library with decent performance on a variety of platforms. We choose triangular solver (TRSM) and matrix multiplication (GEMM) as representative level 3 BLAS routines to implement in OpenCL. We profile TRSM to get the time distribution of the OpenCL runtime system. We then provide tuned GEMM kernels for both the NVIDIA Tesla C2050 and ATI Radeon 5870, the latest GPUs offered by both companies. We explore the benefits of using the texture cache, the performance ramifications of copying data into images, discrepancies in the OpenCL and CUDA compilers' optimizations, and other issues that affect the performance. Experimental results show that nearly 50% of peak performance can be obtained in GEMM on both GPUs in OpenCL. We also show that the performance of these kernels is not highly portable. Finally, we propose the use of auto-tuning to better explore these kernels' parameter space using search harness.

Herein, an ionic liquid-graphene oxide hybrid nanomaterial was prepared via a facile grafting reaction between an imidazole ionic liquid and graphene oxide. FTIR, Raman, and XPS spectra demonstrated that imidazole ionic liquids were successfully covalently attached to the surface of graphene oxide nanosheets. The resulting hybrid nanomaterial, with a thickess of ca. 3 nm, can be stably dispersed in water. Results obtained from electrochemical impedance spectroscopy revealed that ionic liquid-graphene oxide hybrids effectively improved the anticorrosion performance of epoxy-based waterborne coatings. Moreover, ionic liquids performed two functions: (a) they facilitated the dispersion of graphene in a polymer matrix and then utilized the impermeable barrier capability of graphene and (b) they endowed the graphene sheets with a corrosion inhibition effect. The scanning vibrating electrode technique combined with electrochemical tests proved that the enhanced protective performance of the as-prepared composite coatings was attributed to the synergistic effect of the impermeable property of graphene nanosheets and the inhibitory function of the imidazole-based ionic liquid.

Adsorption of three selected pharmaceuticals and personal care products (PPCPs) (ketoprofen (KEP), carbamazepine (CBZ), and bisphenol A (BPA)) by two reduced graphene oxides (rGO1 and rGO2) and one commercial graphene was examined under different solution conditions. Single-walled carbon nanotubes (SWCNTs), multiwalled carbon nanotubes (MWCNTs), and powdered graphite were also investigated for comparison. All adsorption isotherms followed the order of SWCNTs > rGO1 > rGO2 > MWCNTs > graphene > graphite, consistent with the orders of their surface areas and micropore volumes. After surface area normalization, adsorption affinities of the three PPCPs onto graphenes were lower than onto graphite, suggesting incomplete occupation for adsorption sites because of the aggregation of graphene sheets and the presence of oxygen-containing functional groups. The observed pH effects on adsorption correlated well with the pH-regulated distribution of the protonated neutral species of the three PPCPs. Increasing ionic strength from 0 to 20 mM increased KEP adsorption due to the electrostatic screening by Na(+) and Ca(2+). Both humic acid (HA) and sodium dodecylbenzenesulfonate (SDBS) suppressed PPCPs adsorption to all adsorbents, but their impacts onto graphenes were lower than those onto CNTs because of their lower adsorption by graphenes. More severe HA (or SDBS) effect was found on negatively charged KEP at the tested solution pH 6.50 due to the electrostatic repulsion between the same charged KEP and HA (or SDBS). The findings of the present study may have significant implications for the environmental fate assessment of PPCPs and graphene.

Dense matrix factorizations, like LU, Cholesky and QR, are widely used for scientific applications that require solving systems of linear equations, eigenvalues and linear least squares problems. Such computations are normally carried out on supercomputers, whose ever-growing scale induces a fast decline of the Mean Time To Failure (MTTF). This paper proposes a new hybrid approach, based on Algorithm-Based Fault Tolerance (ABFT), to help matrix factorizations algorithms survive fail-stop failures. We consider extreme conditions, such as the absence of any reliable component and the possibility of loosing both data and checksum from a single failure. We will present a generic solution for protecting the right factor, where the updates are applied, of all above mentioned factorizations. For the left factor, where the panel has been applied, we propose a scalable checkpointing algorithm. This algorithm features high degree of checkpointing parallelism and cooperatively utilizes the checksum storage leftover from the right factor protection. The fault-tolerant algorithms derived from this hybrid solution is applicable to a wide range of dense matrix factorizations, with minor modifications. Theoretical analysis shows that the fault tolerance overhead sharply decreases with the scaling in the number of computing units and the problem size. Experimental results of LU and QR factorization on the Kraken (Cray XT5) supercomputer validate the theoretical evaluation and confirm negligible overhead, with-and without-errors.

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