Diagnostic and normalization techniques for laser-generated plumes based on beam deflection and photoacoustic wave measurements

Because of its excellent spatial resolution and sensitivity, the laser microprobe analyzer (LMA) has become an indispensable tool for direct solid analysis. The laser microprobe has been hyphenated to a variety of analytical techniques, in particular, optical spectroscopy and mass spectrometry. However, despite its widespread use, it suffers from relatively poor precision and accuracy;To better understand the laser-solid interactions, especially for those neutral and nonemitting plume species, a new universal detector is developed. The density gradient associated with the transient atomization event is intercepted by a probe laser beam, resulting in beam deflection (BD) in far field. Both the shape and magnitude of the BD signal agree well with the predictions based on a plume model with a radially linear density profile. The probe laser beam can be well focused on the plume, generating a BD signal in a single pass, so both spatial and temporal resolutions are excellent. The plume dynamics (expansion and drift), the spatial density profile, and the amount of evaporated material can be derived with a limit of detection of 1 ng. This technique compares favorably with interferometry for diagnostics of transient atomization events and will find widespread applications;The correlation between atomic emission and photoacoustic wave associated with laser-generated plumes is investigated. Over a widely varied vaporization conditions including laser power, focusing, surface treatment and, to a limited extent, chemical compositions, the amplitude of the photoacoustic wave is linearly related to the atomic emission intensities of both major and minor components. This implies that the photoacoustic signal can be used as an internal standard for the quantitation of laser microprobe analysis

Continuous Arsine Detection Using a Peltier-Effect Cryogenic Trap To Selectively Trap Methylated Arsines

Hydride generation (HG) is an effective technique that eliminates interfering matrix species and enables hydride separation. Arsenic speciation analysis can be fulfilled by cryogenic trapping (CT) based on boiling points of resulting arsines using liquid nitrogen (LN2) as a coolant. In this work, LN2 was replaced by the thermoelectric effect using a cryogenic trap that consisted of a polytetrafluoroethylene (PTFE) body sandwiched by two Peltier modules. After the trap was precooled, the arsines flew along a zigzag channel in the body and reached a sorbent bed of 0.2 g of 15% OV-3 on Chromosorb W-AW-DMCS imbedded near the exit of the trap. CH3AsH2 and (CH3)2AsH were trapped, while AsH3, that passed the trap unaffected, was detected by atomic fluorescence spectrometry. Continuous operation led to enhanced throughput. For inorganic As, the limit of detection (LOD) was 1.1 ng/g and recovery was 101.0 ± 1.1%. Monomethylarsonic acid and dimethylarsinic acid did not interfere with 0.2 ± 1.2% and −0.3 ± 0.5% recoveries, respectively

ORGANOCATALYTIC ASYMMETRIC SUBSTITUTION REACTIONS OF MORITA-BAYLIS-HILLMAN CARBONATES

Ph.DDOCTOR OF PHILOSOPH

Scalable Freeze-Tape-Casting Fabrication and Pore Structure Analysis of 3D LLZO Solid-State Electrolytes.

Nonflammable solid-state electrolytes can potentially address the reliability and energy density limitations of lithium-ion batteries. Garnet-structured oxides such as Li7La3Zr2O12 (LLZO) are some of the most promising candidates for solid-state devices. Here, three-dimensional (3D) solid-state LLZO frameworks with low tortuosity pore channels are proposed as scaffolds, into which active materials and other components can be infiltrated to make composite electrodes for solid-state batteries. To make the scaffolds, we employed aqueous freeze tape casting (FTC), a scalable and environmentally friendly method to produce porous LLZO structures. Using synchrotron radiation hard X-ray microcomputed tomography, we confirmed that LLZO films with porosities of up to 75% were successfully fabricated from slurries with a relatively wide concentration range. The acicular pore size and shape at different depths of scaffolds were quantified by fitting the pore shapes with ellipses, determining the long and short axes and their ratios, and investigating the equivalent diameter distribution. The results show that relatively homogeneous pore sizes and shapes were sustained over a long range along the thickness of the scaffold. Additionally, these pores had low tortuosity and the wall thickness distributions were found to be highly homogeneous. These are desirable characteristics for 3D solid electrolytes for composite electrodes, in terms of both the ease of active material infiltration and also minimization of Li diffusion distances in electrodes. The advantages of the FTC scaffolds are demonstrated by the improved conductivity of LLZO scaffolds infiltrated with poly(ethylene oxide)/lithium bis(trifluoromethanesulfonyl)imide (PEO/LITFSI) compared to those of PEO/LiTFSI films alone or composites containing LLZO particles

Learning Graph Convolutional Network for Skeleton-based Human Action Recognition by Neural Searching

Human action recognition from skeleton data, fueled by the Graph Convolutional Network (GCN), has attracted lots of attention, due to its powerful capability of modeling non-Euclidean structure data. However, many existing GCN methods provide a pre-defined graph and fix it through the entire network, which can loss implicit joint correlations. Besides, the mainstream spectral GCN is approximated by one-order hop, thus higher-order connections are not well involved. Therefore, huge efforts are required to explore a better GCN architecture. To address these problems, we turn to Neural Architecture Search (NAS) and propose the first automatically designed GCN for skeleton-based action recognition. Specifically, we enrich the search space by providing multiple dynamic graph modules after fully exploring the spatial-temporal correlations between nodes. Besides, we introduce multiple-hop modules and expect to break the limitation of representational capacity caused by one-order approximation. Moreover, a sampling- and memory-efficient evolution strategy is proposed to search an optimal architecture for this task. The resulted architecture proves the effectiveness of the higher-order approximation and the dynamic graph modeling mechanism with temporal interactions, which is barely discussed before. To evaluate the performance of the searched model, we conduct extensive experiments on two very large scaled datasets and the results show that our model gets the state-of-the-art results.Comment: Accepted by AAAI202

The simultaneous repair of an Irreducible Diaphragmatic Hernia while carrying out a Cesarean Section

AbstractINTRODUCTIONDiaphragmatic hernia complicating pregnancy rarely occurs while it is frequently misdiagnosed.PRESENTATION OF CASEA pregnant woman who had suffered from recurrent right upper-quadrant abdominal pain for 4 months was hospitalized near full term because the unrelieved abdominal pain was so severe that she couldn’t lie down. Following the emergency caesarean, we found a part of the transverse colon and a part of omentum were trapped in the thorax through a 3cm by 3cm laceration in the patient's diaphragm. We removed all trapped intestine which was about 40cm long and repaired diaphragmatic hernia at the same time.DISCUSSIONRadiography is useful to diagonisis diaphragmatic hernia, but it had little use for pregnant women. An irreducible diaphragmatic hernia represent a surgical emergency irrespective of fetal maturity. In our case, she had her hernia repaired just during caesarean section by laparotomy.CONCLUSIONCareful examination and a timely operation are needed to treat diaphragmatic hernia complicating pregnancy

Multi-level decision framework collision avoidance algorithm in emergency scenarios

With the rapid development of autonomous driving, the attention of academia has increasingly focused on the development of anti-collision systems in emergency scenarios, which have a crucial impact on driving safety. While numerous anti-collision strategies have emerged in recent years, most of them only consider steering or braking. The dynamic and complex nature of the driving environment presents a challenge to developing robust collision avoidance algorithms in emergency scenarios. To address the complex, dynamic obstacle scene and improve lateral maneuverability, this paper establishes a multi-level decision-making obstacle avoidance framework that employs the safe distance model and integrates emergency steering and emergency braking to complete the obstacle avoidance process. This approach helps avoid the high-risk situation of vehicle instability that can result from the separation of steering and braking actions. In the emergency steering algorithm, we define the collision hazard moment and propose a multi-constraint dynamic collision avoidance planning method that considers the driving area. Simulation results demonstrate that the decision-making collision avoidance logic can be applied to dynamic collision avoidance scenarios in complex traffic situations, effectively completing the obstacle avoidance task in emergency scenarios and improving the safety of autonomous driving