IR-MALDESI Mass Spectrometry Imaging at 50 Micron Spatial Resolution

High spatial resolution in mass spectrometry imaging (MSI) is crucial to understanding the biology dictated by molecular distributions in complex tissue systems. Here, we present MSI using infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) at 50 μm resolution. An adjustable iris, beam expander, and an aspherical focusing lens were used to reduce tissue ablation diameters for MSI at high resolution. The laser beam caustic was modeled using laser ablation paper to calculate relevant laser beam characteristics. The minimum laser spot diameter on the tissue was determined using tissue staining and microscopy. Finally, the newly constructed optical system was used to image hen ovarian tissue with and without oversampling, detailing tissue features at 50 μm resolution.

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Synthesis of core–shell magnetic ion-imprinted polymer nanospheres for selective solid-phase extraction of Pb2+ from biological,food, and wastewater samples

Magnetic ion-imprinted polymer nanospheres, which have core–shell structures, have been synthesized as an adsorbent for extraction of Pb²⁺ from real samples prior to its flame atomic absorption spectrometric determination. The prepared adsorbent has been characterized using XRD, VSM, TEM, and FTIR measurements. The optimization results revealed that the adsorbent exhibited high selectivity toward Pb²⁺ over other cations such as Cu²⁺ and Zn²⁺. In addition, the removal efficiency of synthesized adsorbent was considerable (q_m?=?171.42?mg g^?1), its calibration curve was linear (0.5?850.0?ng mL^?1), and detection limit was 0.01?ng mL^?1. These results suggested that the prepared nanoadsorbent is an ideal candidate for solid-phase extraction of Pb²⁺ ions.     

Accelerated AI development for autonomous materials synthesis in flow

Autonomous robotic experimentation strategies are rapidly rising in use because, without the need for user intervention, they can efficiently and precisely converge onto optimal intrinsic and extrinsic synthesis conditions for a wide range of emerging materials. However, as the material syntheses become more complex, the meta-decisions of artificial intelligence (AI)-guided decision-making algorithms used in autonomous platforms become more important. In this work, a surrogate model is developed using data from over 1000 in-house conducted syntheses of metal halide perovskite quantum dots in a self-driven modular microfluidic material synthesizer. The model is designed to represent the global failure rate, unfeasible regions of the synthesis space, synthesis ground truth, and sampling noise of a real robotic material synthesis system with multiple output parameters (peak emission, emission linewidth, and quantum yield). With this model, over 150 AI-guided decision-making strategies within a single-period horizon reinforcement learning framework are automatically explored across more than 600 000 simulated experiments – the equivalent of 7.5 years of continuous robotic operation and 400 L of reagents – to identify the most effective methods for accelerated materials development with multiple objectives. Specifically, the structure and meta-decisions of an ensemble neural network-based material development strategy are investigated, which offers a favorable technique for intelligently and efficiently navigating a complex material synthesis space with multiple targets. The developed ensemble neural network-based decision-making algorithm enables more efficient material formulation optimization in a no prior information environment than well-established algorithms.

A surrogate model is designed to represent a microfluidic material synthesis system using 1000 automatically conducted experiments. With this model, over 600 000 experiments are simulated to optimize an AI-guided material synthesis algorithm.     

Synthesis of (E)-2-amino-N′-benzylidenehexahydroquinoline-3-carbohydrazide

Molecular Diversity - A one-pot, multi-component protocol for the synthesis of a new class of functionalized quinoline carbohydrazide derivatives via reaction of various anilines, dimedone,...     

Simulating pedestrian flow through narrow exits

Introduction: The flow of pedestrians through narrow doorways is one of the most common features of crowd motions and evacuations. It is particularly an important aspect of pedestrian simulations models since their accuracy depends highly on their ability to produce realistic exit flow rates. The problem has been extensively studied in the literature, but many aspects of it have remained controversial with mixed (and often contradictory) evidence emerging from different studies and different methods. Methods: We discuss the significance of parameter calibration for accurate simulation of pedestrian flow through narrow exits using social force model. Based on sensitivity analyses, we show how simulated exit throughput rate can vastly differ by changing the value of certain parameters. We identify the two parameters that are most critical, and then calibrate them based on a set of experimental observations (at macro level). Using these calibrated parameters, we then re-examine three fundamental questions related to pedestrian flow at bottlenecks, (1) the relation between desired velocity and simulated egress time; (2) the effect of barricade at exits; and (3) the effect of exit in the corner versus the middle. Results: Our numerical analyses showed that, with the calibrated parameters, increasing the desired velocity in the social-force model results in monotonically shorter egress times (at a marginal rate that rapidly diminishes as the desired velocity increases). We showed that placing a panel-like barricade at exit can facilitate the outflow and reduces the egress time, but its effect depends on the widths of exit, as well as the size of the barricade and its distance to exit. We show that the positioning the exit in the corner is also effective in terms of reducing egress time, but only for very narrow exits. The benefit diminishes quickly as the exit becomes wider. Applications: These outcomes demonstrated the significance of parameter calibration for accurate simulation of crowd flows. The findings may also help to identify simple modifications that can facilitate crowd flows at narrow bottlenecks.     

Pentafluorophenylammonium triflate as a suitable and effective metal-free catalyst for the synthesis of quinazoline derivatives via one-pot multicomponent method

Research on Chemical Intermediates - A simple and facile synthesis of highly functionalized quinazoline derivatives has been successfully developed by treatment of aldehydes, ammonium acetate, and...     

Preparation of Carvedilol Nanoparticles by Emulsification Method and Optimization of Drug Release: Surface Response Design Versus Genetic Algorithm

The objectives of this research were the production of Eudragit nanoparticles of carvedilol, an anti-hypertension drug, for enhancement of its absorption and optimization of drug release. Nanoparticles were prepared by emulsification-solvent evaporation or diffusion methods. The statistical surface response design, based on the Box-Behnken model, was applied to evaluate the effect of four variables, each in two levels, on specifications of nanoparticles. An intelligent modeling system was established according to genetic algorithm to predict drug release from the nanoparticles. The neural network-genetic algorithm model showed a more precise method than surface response design in the prediction of the release properties of carvedilol from Eudragit nanoparticles.