FlowBO: A Flow Chemistry Bayesian Optimization Framework Benchmarked by Kinetic Models

The applications of flow chemistry (continuous flow reactions) in the synthesis of pharmaceuticals and fine chemicals require more advanced optimization algorithms to guide laboratory-scale and industry-scale optimization. Although several Bayesian Optimization (BO) frameworks have been developed, they are rarely equipped with state-of-the-art noise-handling acquisition functions and have not been benchmarked by multiple real-world continuous flow kinetic models. In this study, we developed FlowBO for flow chemistry, equipped with the recently-developed MOO algorithm qNEHVI that can better handle experimental noise and make parallel recommendations. Also, five kinetic models built from experimental results, including four series reactions, were used as benchmarks for FlowBO and two other recognized BO frameworks. The results show that FlowBO outperforms in all four series reaction cases with optimization results >99.9% for conversion and selectivity. At the same time, FlowBO offers a range of optimum advantages with a wide choice of temperature, residence time, and reactant concentration, facilitating process optimization for subsequent steps (i.e. separation)

Real-Time Monitoring Method of Strawberry Fruit Growth State Based on YOLO Improved Model

A key challenge in automated orchard management robots is the fast and accurate identification of crop growth conditions and maturity for subsequent operations such as automatic pollination, fertilization and picking. In particular, strawberry fruits have a short ripening period and the fruits are heavily overlapped and shaded by each other, which is time-consuming and ineffective based on traditional detection methods. Therefore, we designed and developed a strawberry growth detection algorithm, SDNet (Strawberry Detect Net). The algorithm is based on the YOLOX model and replaces the original CSP block in the backbone network with a self-designed feature extraction module C3HB block to improve the spatial interaction capability and monitoring accuracy of the detection algorithm; Then, the normalized attention module (NAM) is embedded in the neck to improve the detection accuracy and attention weight of small target fruits; and we use the latest SIOU objective loss function to improve the prediction accuracy of the detection model, which finally achieves the monitoring of strawberry fruits under five growth states. The experimental results show that the precision, accuracy, and recall of SDNet are 94.26%, 93.15%, and 90.72%, respectively, and the monitoring speed is 30.5 ms. It is 4.08%, 3.64 and 2.04% higher than the precision, accuracy, and recall of YOLOX, respectively, and there is no significant change in the model size. The research results can effectively solve the problem of low accuracy of strawberry fruit growth state monitoring under complex environments, and provide important technical reference for realizing unmanned farm and precision agriculture

Optimizing telescoped heterogeneous catalysis with noise-resilient multi-objective Bayesian optimization

This study evaluates the noise resilience of multi-objective Bayesian optimization (MOBO) algorithms in chemical synthesis, an aspect critical for processes like telescoped reactions and heterogeneous catalysis but seldom systematically assessed. Through simulation experiments on amidation, acylation, and SNAr reactions under varying noise levels, we identify the qNEHVI acquisition function as notably proficient in handling noise. Subsequently, qNEHVI is employed to optimize a two-step heterogeneous catalysis for the continuous-flow synthesis of hexafluoroisopropanol. Achieving considerable optimization within just 20 experimental runs, we report an E-factor of 0.3744 and a conversion rate of 76.20%, with optimal conditions set at 5.00 sccm and 35.00℃ for the first step, and 80.00 sccm and 170℃ for the second. This research highlights qNEHVI\u27s potential in noisy multi-objective optimization and its practical utility in refining complex synthesis processes

Radar cross-section measurements of ice particles using vector network analyzer

We carried out radar cross-section (RSC) measurements of ice particles in a microwave anechoic chamber at Nanjing University of Information Science and Technology. We used microwave similarity theory to enlarge the size of particle from the micrometer to millimeter scale and to reduce the testing frequency from 94 GHz to 10 GHz. The microwave similarity theory was validated using the method of moments for single metal sphere, single dielectric sphere, and spherical and non-spherical dielectric particle swarms. The differences between the retrieved and theoretical results at 94 GHz were 0.016117%, 0.0023029%, 0.027627%, and 0.0046053%, respectively. We proposed a device that can measure the RCS of ice particles in the chamber based on the S21 parameter obtained from vector network analyzer. On the basis of the measured S21 parameter of the calibration material (metal plates) and their corresponding theoretical RCS values, the RCS values of a spherical Teflon particle swarm and cuboid candle particle swarm was retrieved at 10 GHz. In this case, the differences between the retrieved and theoretical results were 12.72% and 24.49% for the Teflon particle swarm and cuboid candle swarm, respectively

Analysis of LRRK2, SNCA, and ITGA8 Gene Variants with Sporadic Parkinson’s Disease Susceptibility in Chinese Han Population

Background. Parkinson’s disease (PD) is an age-related neurodegenerative disease affected by multiple genetic and environmental factors. We performed a case-control study on candidate gene to scrutinize whether genetic variants in LRRK2, SNCA, and ITGA8 genes could be associated with sporadic PD in Chinese Han population. Methods. Five single-nucleotide polymorphisms (SNPs) of LRRK2 (rs1491942), SNCA (rs2301134, rs2301135, and rs356221), and ITGA8 (rs7077361) were selected and genotyped among 583 unrelated PD patients and 558 healthy controls. Results. Rs1491942 of LRRK2 gene had a significantly higher genotype frequency (P=3.543E-09) and allelic G/C frequencies (P=2.601E-10) in PD patients than controls. Rs2301135 of SNCA gene also showed an obvious difference in genotype frequency (P=4.394E-07) and allelic G/C frequencies (P=9.116E-13) between PD patients and controls. SNPs rs2301134 and rs356221 of SNCA gene and rs7077361 of ITGA8 gene lacked the significant association with the susceptibility of PD in Chinese Han population. Conclusions. Our study firstly expresses that rs1491942 of LRRK2 and rs2301135 of SNCA gene are substantially associated with sporadic Parkinson’s disease in Chinese Han population

Enabling 4.6 V LiNi0.6Co0.2Mn0.2O2 cathodes with excellent structural stability: combining surface LiLaO2 self-assembly and subsurface La-pillar engineering

Although Ni-rich layered materials with the general formula LiNi1-x-yCoxMnyO2 (0 < x, y < 1, NCM) hold great promise as high-energy-density cathodes in commercial lithium-ion batteries, their practical application is greatly hampered by poor cyclability and safety. Herein, a LiNi0.6Co0.2Mn0.2O2 (NCM622) cathode modified with a surface self-assembling LiLaO2 coating and subsurface La pillars demonstrates stabilized cycling at 4.6 V. The LiLaO2-coated NCM622 benefits from the suppression of interfacial side reactions, which relieves the layer-to-rock salt phase transformation and therefore improves the capacity retention under high voltages. Moreover, the La dopant, as a pillar in the NCM622 lattice, plays a dual role in expanding the c lattice parameter to enhance the Li-ion diffusion capability, as well as suppressing Ni antisite defect formation upon cycling. Consequently, the dual-modified NCM622 cathode exhibits an initial Coulombic efficiency of over 85% and a high capacity of over 200 mAh g-1 at 0.1 C. A specific capacity of 188 mAh g-1 with a capacity retention of 76% is achieved at 1 C after 200 cycles within a voltage range of 3.0-4.6 V. These findings lay a solid foundation for the materials design and performance optimization of high-energy-density cathodes for Li-ion batteries

Development and Characterization of a Novel Peptide—Drug Conjugate with DM1 for Treatment of FGFR2-Positive Tumors

A maytansin derivative, DM1, is a promising therapeutic compound for treating tumors, but is also a highly poisonous substance with various side effects. For clinical expansion, we tried to develop novel peptide-drug conjugates (PDCs) with DM1. In the study, a one-bead one-compound (OBOC) platform was used to screen and identify a novel, highly stable, non-natural amino acid peptide targeting the tyrosine receptor FGFR2. Then, the identified peptide, named LLC2B, was conjugated with the cytotoxin DM1. Our results show that LLC2B has high affinity for the FGFR2 protein according to an isothermal titration calorimetry (ITC) test. LLC2B-Cy5.5 binding to FGFR2-positive cancer cells was confirmed by fluorescent microscopic imaging and flow cytometry in vitro. Using xenografted nude mouse models established with breast cancer MCF-7 cells and esophageal squamous cell carcinoma KYSE180 cells, respectively, LLC2B-Cy5.5 was observed to specifically target tumor tissues 24 h after tail vein injection. Incubation assays, both in aqueous solution at room temperature and in human plasma at 37 °C, suggested that LLC2B has high stability and strong anti-proteolytic ability. Then, we used two different linkers, one of molecular disulfide bonds and another of a maleimide group, to couple LLC2B to the toxin DM1. The novel peptide-drug conjugates (PDCs) inhibited tumor growth and significantly increased the maximum tolerated dose of DM1 in xenografted mice. In brief, our results suggest that LLC2B-DM1 can be developed into a potential PDC for tumor treatment in the future