Persistence Length Control of the Polyelectrolyte Layer-by-Layer Self-Assembly on Carbon Nanotubes

One-dimensional inorganic materials such as carbon nanotubes1 and semiconductor nanowires have been central to important advances in materials science in the last decade. Unique mechanical and electronic properties of these molecular-scale wires enabled a variety of applications ranging from novel composite materials, to electronic circuits, to new sensors. Often, these applications require non-covalent modification of carbon nanotubes with organic compounds, DNA and biomolecules, and polymers to change nanotube properties or to add new functionality. We recently demonstrated a versatile and flexible strategy for non-covalent modification of carbon nanotubes using layer-by-layer self-assembly of polyelectrolytes. Researchers used this technique extensively for modification of flat surfaces, micro-, and nano-particles; however, little is known about the mechanism and the factors influencing layer-by-layer self-assembly in one-dimensional nanostructures. The exact conformation of polyelectrolyte chains deposited on single-walled carbon nanotubes (SWNT) is still unknown. There are two possible configurations: flexible polymers wrapping around the nanotube and stretched, rigid chains stacked parallel to the nanotube axis. Several factors, such as polymer rigidity, surface curvature, and strength of polymer-surface interactions, can determine the nature of assembly. Persistence length of the polymer chain should be one of the critical parameters, since it determines the chain's ability to wrap around the nanotube. Indeed, computer simulations for spherical substrates show that polymer rigidity and substrate surface curvature can influence the deposition process. Computational models also show that the persistence length of the polymer must fall below the threshold values determined by target surface curvature in order to initiate polyelectrolyte deposition process. Although these models described the effects of salt concentration and target surface curvature, they considered only nano-particles with radius 5 nanometer and larger. One-dimensional materials, such as carbon nanotubes, provide an even more interesting template for studying self-assembly mechanisms, since they give us access to even smaller surface curvatures down to 1 nm. We have examined the role of the polymer persistence length in layer-by-layer self-assembly process on carbon nanotubes by observing formation of multilayer polyelectrolyte shells around carbon nanotubes at different ionic strength. Persistence length of polyelectrolytes varies with solution ionic strength, due to screening of the electrostatic repulsion between the polymer Figure 1. TEM images of single-walled carbon nanotubes after polymer deposition for ionic strengths of (A) 0.05M, (B) 0.1M, (C) 0.2M, (D) 0.4M, (E) 0.65M, and (F) 1.05M. Scale bar corresponds to 10 nm. backbone charges; therefore changing ionic strength is a convenient way to alter the configuration of the polymer molecule systematically. We have used the layer-by-layer self-assembly technique to form 5-layer thick coating of the alternating polyallylamine hydrochloride (PAH) and sodium poly(styrenesulfonate) (PSS) layers on the surfaces of the pristine single-wall carbon nanotubes. For our experiments, we grew the nanotubes across copper TEM grid openings using catalytic chemical vapor deposition. The deposition solutions contained different amounts of NaCl to vary the ionic strength. After polymer multilayer formation we examined the resulting coating in high-resolution TEM

Performance evaluation of inpatient service in Beijing: a horizontal comparison with risk adjustment based on Diagnosis Related Groups

<p>Abstract</p> <p>Background</p> <p>The medical performance evaluation, which provides a basis for rational decision-making, is an important part of medical service research. Current progress with health services reform in China is far from satisfactory, without sufficient regulation. To achieve better progress, an effective tool for evaluating medical performance needs to be established. In view of this, this study attempted to develop such a tool appropriate for the Chinese context.</p> <p>Methods</p> <p>Data was collected from the front pages of medical records (FPMR) of all large general public hospitals (21 hospitals) in the third and fourth quarter of 2007. Locally developed Diagnosis Related Groups (DRGs) were introduced as a tool for risk adjustment and performance evaluation indicators were established: Charge Efficiency Index (CEI), Time Efficiency Index (TEI) and inpatient mortality of low-risk group cases (IMLRG), to reflect respectively work efficiency and medical service quality. Using these indicators, the inpatient services' performance was horizontally compared among hospitals. Case-mix Index (CMI) was used to adjust efficiency indices and then produce adjusted CEI (aCEI) and adjusted TEI (aTEI). Poisson distribution analysis was used to test the statistical significance of the IMLRG differences between different hospitals.</p> <p>Results</p> <p>Using the aCEI, aTEI and IMLRG scores for the 21 hospitals, Hospital A and C had relatively good overall performance because their medical charges were lower, LOS shorter and IMLRG smaller. The performance of Hospital P and Q was the worst due to their relatively high charge level, long LOS and high IMLRG. Various performance problems also existed in the other hospitals.</p> <p>Conclusion</p> <p>It is possible to develop an accurate and easy to run performance evaluation system using Case-Mix as the tool for risk adjustment, choosing indicators close to consumers and managers, and utilizing routine report forms as the basic information source. To keep such a system running effectively, it is necessary to improve the reliability of clinical information and the risk-adjustment ability of Case-Mix.</p

Fast Distributed Inference Serving for Large Language Models

Large language models (LLMs) power a new generation of interactive AI applications exemplified by ChatGPT. The interactive nature of these applications demand low job completion time (JCT) for model inference. Existing LLM serving systems use run-to-completion processing for inference jobs, which suffers from head-of-line blocking and long JCT. We present FastServe, a distributed inference serving system for LLMs. FastServe exploits the autoregressive pattern of LLM inference to enable preemption at the granularity of each output token. FastServe uses preemptive scheduling to minimize JCT with a novel skip-join Multi-Level Feedback Queue scheduler. Based on the new semi information-agnostic setting of LLM inference, the scheduler leverages the input length information to assign an appropriate initial queue for each arrival job to join. The higher priority queues than the joined queue are skipped to reduce demotions. We design an efficient GPU memory management mechanism that proactively offloads and uploads intermediate states between GPU memory and host memory for LLM inference. We build a system prototype of FastServe based on NVIDIA FasterTransformer. Experimental results show that compared to the state-of-the-art solution Orca, FastServe improves the average and tail JCT by up to 5.1$\times$ and 6.4$\times$, respectively