Scalable decarboxylative trifluoromethylation by ion-shielding heterogeneous photoelectrocatalysis.

Electrochemistry offers a sustainable synthesis route to value-added fine chemicals but is often constrained by competing electron transfer between the electrode and redox-sensitive functionalities distinct from the target site. Here, we describe an ion-shielding heterogeneous photoelectrocatalysis strategy to impose mass-transfer limitations that invert the thermodynamically determined order of electron transfer. This strategy is showcased to enable decarboxylative trifluoromethylation of sensitive (hetero)arenes by using trifluoroacetate, an inexpensive yet relatively inert trifluoromethyl group (CF3) source. An ion-shielding layer, formed by trifluoroacetate anions electrostatically adsorbed on a positive molybdenum-doped tungsten trioxide (WO3) photoanode, prevents undesired electron transfer between substrates and photogenerated holes. The practicality of the developed method was demonstrated with robust photoanode stability (approximately 380 hours), a good substrate scope, and scaling capability to achieve 100-gram synthesis by using photoelectrochemical flow cells.

Direct prediction of gas adsorption via spatial atom interaction learning.

Physisorption relying on crystalline porous materials offers prospective avenues for sustainable separation processes, greenhouse gas capture, and energy storage. However, the lack of end-to-end deep learning model for adsorption prediction confines the rapid and precise screen of crystalline porous materials. Here, we present DeepSorption, a spatial atom interaction learning network that realizes accurate, fast, and direct structure-adsorption prediction with only information of atomic coordinate and chemical element types. The breakthrough in prediction is attributed to the awareness of global structure and local spatial atom interactions endowed by the developed Matformer, which provides the intuitive visualization of atomic-level thinking and executing trajectory in crystalline porous materials prediction. Complete adsorption curves prediction could be performed using DeepSorption with a higher accuracy than Grand canonical Monte Carlo simulation and other machine learning models, a 20-35% decline in the mean absolute error compared to graph neural network CGCNN and machine learning models based on descriptors. Since the established direct associations between raw structure and target functions are based on the understanding of the fundamental chemistry of interatomic interactions, the deep learning network is rationally universal in predicting the different physicochemical properties of various crystalline materials.

Wireless Electrochemical Reactor for Accelerated Exploratory Study of Electroorganic Synthesis.

Electrosynthesis is an emerging tool to construct value-added fine chemicals under mild and sustainable conditions. However, the complex apparatus required impedes the facile development of new electrochemistry in the laboratory. Herein, we proposed and demonstrated the concept of wireless electrochemistry (Wi-eChem) based on wireless power transfer technology. The core of this concept is the dual-function wireless electrochemical magnetic stirrer that provides an electrolysis driving force and mixing simultaneously in a miniaturized form factor. This Wi-eChem system allowed electrochemists to execute electrochemical reactions in a manner similar to traditional organic chemistry without handling wire connections. The controllability, reusability, and versatility were validated with a series of modern electrosynthesis reactions, including electrodecarboxylative etherification, electroreductive olefin-ketone coupling, and electrochemical nickel-catalyzed oxygen atom transfer reaction. Its remarkably simplified operation enabled its facile integration into a fully automated robotic synthesis platform to achieve autonomous parallel electrosynthesis screening.

AROPS: A Framework of Automated Reaction Optimization with Parallelized Scheduling.

With the development of automated experimental platforms and optimization algorithms, chemists can easily optimize chemical reactions in an automated and high-throughput fashion. However, the modules in existing automated experimental platforms are operated in a linear fashion without orchestrating with the optimization algorithm, thus leaving room for further efficiency improvement. Here, we introduced a framework of automated reaction optimization with parallelized scheduling (AROPS) to realize the integration of the optimization algorithm and module scheduling. AROPS relies on a customized Bayesian optimizer to solve multi-reactor/analyzer reaction optimization problems with three different scheduling modes to arrange tasks for various experimental modules. In addition, a mechanism based on probability of improvement (PI) for discarding unpromising ongoing experiments was developed to facilitate freeing up valuable experimental resources in parallelized optimization. We tested the performance of AROPS using a hardware emulator on three representative benchmark reactions encountered in organic synthesis, illustrating that AROPS can trade off optimization time and cost according to the chemists' preference.

Machine-Learning-Guided Discovery of Electrochemical Reactions.

The molecular structures synthesizable by organic chemists dictate the molecular functions they can create. The invention and development of chemical reactions are thus critical for chemists to access new and desirable functional molecules in all disciplines of organic chemistry. This work seeks to expedite the exploration of emerging areas of organic chemistry by devising a machine-learning-guided workflow for reaction discovery. Specifically, this study uses machine learning to predict competent electrochemical reactions. To this end, we first develop a molecular representation that enables the production of general models with limited training data. Next, we employ automated experimentation to test a large number of electrochemical reactions. These reactions are categorized as competent or incompetent mixtures, and a classification model was trained to predict reaction competency. This model is used to screen 38,865 potential reactions in silico, and the predictions are used to identify a number of reactions of synthetic or mechanistic interest, 80% of which are found to be competent. Additionally, we provide the predictions for the 38,865-member set in the hope of accelerating the development of this field. We envision that adopting a workflow such as this could enable the rapid development of many fields of chemistry.

Effects of Ethanol Treatment on Storage Quality and Antioxidant System of Postharvest Papaya.

Papaya is the fourth most favored tropical fruit in the global market; it has rich nutrition and can be used for medicine and food processing. However, it will soften and mature in a short time after harvest, resulting in a lot of economic losses. In this study, papaya fruits were soaked in 0, 12.5, 25, 50, and 100 ml/L ethanol solutions for 2 h and stored at 25°C for 14 days, by which we explored the effects of ethanol treatment in papaya after harvest. At an optimal concentration of ethanol treatment, color changing of the papaya fruits was delayed for 6 days, and decay incidence and average firmness of the fruits were shown as 20% and 27.7 N, respectively. Moreover, the effect of ethanol treatment on antioxidant systems in the papaya fruits was explored. It was observed that ethanol treatment contributed to diminish the development of malondialdehyde (MDA), ethylene, and superoxide anions. Furthermore, the activities of superoxide dismutase (SOD), catalase (CAT), and ascorbate peroxidase (APX) were promoted than those of control group, while the activities of peroxidase (POD), phenylalanine ammonia-lyase (PAL), and polyphenol oxidase (PPO) were brought down. In addition, the principal component analysis (PCA) showed that PAL, ethylene, and superoxide anions were the main contributors for the maturity and senescence of postharvest papaya. In this experiment, ethanol treatment had the potential of delaying the ripening and maintaining the storage quality of papaya fruits.

A Multifunctional Microfluidic Platform for High-Throughput Experimentation of Electroorganic Chemistry.

Electroorganic synthesis is a promising tool to design sustainable transformations and discover new reactivities. However, the added setup complexity caused by electrodes in the system impedes efficient screening of reaction conditions. Herein, we present a microfluidic platform that enables automated high-throughput experimentation (HTE) for electroorganic synthesis at a 15-microliter scale. Two HTE modules are demonstrated: 1) the rapid electrochemical reaction condition screening for a radical-radical cross-coupling reaction on micro-fabricated interdigitated electrodes, and 2) measurements of kinetics for mediated anodic oxidations using the microliter-scale cyclic voltammetry. The presented modular approach could be deployed for a range of other electroorganic chemistry applications beyond the demonstrated functionalities.

Microfluidic electrochemistry for single-electron transfer redox-neutral reactions.

Electrochemistry offers opportunities to promote single-electron transfer (SET) redox-neutral chemistries similar to those recently discovered using visible-light photocatalysis but without the use of an expensive photocatalyst. Herein, we introduce a microfluidic redox-neutral electrochemistry (µRN-eChem) platform that has broad applicability to SET chemistry, including radical-radical cross-coupling, Minisci-type reactions, and nickel-catalyzed C(sp2)-O cross-coupling. The cathode and anode simultaneously generate the corresponding reactive intermediates, and selective transformation is facilitated by the rapid molecular diffusion across a microfluidic channel that outpaces the decomposition of the intermediates. µRN-eChem was shown to enable a two-step gram-scale electrosynthesis of a nematic liquid crystal compound, demonstrating its practicality.

Towards efficient discovery of green synthetic pathways with Monte Carlo tree search and reinforcement learning.

Computer aided synthesis planning of synthetic pathways with green process conditions has become of increasing importance in organic chemistry, but the large search space inherent in synthesis planning and the difficulty in predicting reaction conditions make it a significant challenge. We introduce a new Monte Carlo Tree Search (MCTS) variant that promotes balance between exploration and exploitation across the synthesis space. Together with a value network trained from reinforcement learning and a solvent-prediction neural network, our algorithm is comparable to the best MCTS variant (PUCT, similar to Google's Alpha Go) in finding valid synthesis pathways within a fixed searching time, and superior in identifying shorter routes with greener solvents under the same search conditions. In addition, with the same root compound visit count, our algorithm outperforms the PUCT MCTS by 16% in terms of determining successful routes. Overall the success rate is improved by 19.7% compared to the upper confidence bound applied to trees (UCT) MCTS method. Moreover, we improve 71.4% of the routes proposed by the PUCT MCTS variant in pathway length and choices of green solvents. The approach generally enables including Green Chemistry considerations in computer aided synthesis planning with potential applications in process development for fine chemicals or pharmaceuticals.

Evaluating and clustering retrosynthesis pathways with learned strategy.

With recent advances in the computer-aided synthesis planning (CASP) powered by data science and machine learning, modern CASP programs can rapidly identify thousands of potential pathways for a given target molecule. However, the lack of a holistic pathway evaluation mechanism makes it challenging to systematically prioritize strategic pathways except for using some simple heuristics. Herein, we introduce a data-driven approach to evaluate the relative strategic levels of retrosynthesis pathways using a dynamic tree-structured long short-term memory (tree-LSTM) model. We first curated a retrosynthesis pathway database, containing 238k patent-extracted pathways along with ∼55 M artificial pathways generated from an open-source CASP program, ASKCOS. The tree-LSTM model was trained to differentiate patent-extracted and artificial pathways with the same target molecule in order to learn the strategic relationship among single-step reactions within the patent-extracted pathways. The model achieved a top-1 ranking accuracy of 79.1% to recognize patent-extracted pathways. In addition, the trained tree-LSTM model learned to encode pathway-level information into a representative latent vector, which can facilitate clustering similar pathways to help illustrate strategically diverse pathways generated from CASP programs.

Enterobactin-Specific Antibodies Induced by a Novel Enterobactin Conjugate Vaccine.

Enterobactin (Ent)-mediated high-affinity iron acquisition is critical for Gram-negative bacteria to survive in the host. Given the bacteriostatic effect of lipocalin resulting from its potent Ent-binding ability, immune intervention directly targeting Ent is promising for iron-dependent pathogen control. Recently, an Ent conjugate vaccine was reported, but it still has several significant weaknesses. In this study, we sought to develop an innovative Ent conjugate vaccine that can induce a high level of antibodies directed against Ent and to provide solid evidence demonstrating siderophore-binding capacity of Ent-specific antibodies. Using a simple method, we successfully conjugated purified Ent to different carriers, including keyhole limpet hemocyanin (KLH), bovine serum albumin, and CmeC, a vaccine candidate for Campylobacter control. Subcutaneous immunization of rabbits with the KLH-Ent conjugate triggered a strong systemic IgG immune response with an up to 16,384-fold increase in IgG titer directed against whole conjugate and an up to 4,096-fold increase in the level of specific anti-Ent IgG. To evaluate the ability of Ent-specific IgG to bind to the Ent derivatives present in vivo, various Ent derivatives were chemically synthesized and a unique enzyme-linked immunosorbent assay method was developed. The Ent-specific IgG also displayed exceptional reactivity to ferric Ent, a linear trimer of Ent, and different salmochelins. Growth assays further demonstrated that the Ent-specific antibodies significantly inhibited Ent-dependent growth of Campylobacter spp. and Escherichia coli Collectively, this study reports an efficient method to prepare a new type of Ent conjugate vaccines for inducing a high level of Ent-specific antibodies, which can bind to various Ent derivatives and display lipocalin-like bacteriostatic features.IMPORTANCE Ent-mediated high-affinity iron acquisition is a universal and critical contributor for Gram-negative pathogens to survive and infect hosts. Published information has supported an innovative immune intervention strategy that directly targets Ent to starve pathogens by limiting the availability of iron to be utilized. Compared to a recently published Ent conjugate, there are three advantages of the vaccine described in this study: ease of preparation, induction of high titer of anti-Ent IgG, and the ability of Ent-specific antibodies to bind various Ent derivatives, including the salmochelins that help enteric pathogens evade sequestration of siderophores by host lipocalins. In addition, the Ent-specific antibodies were demonstrated to function similarly to lipocalin to interfere with the Ent-dependent growth of Campylobacter and E. coli under iron-restricted conditions. This study has significant potential for broader applications to prevent and control various Gram-negative infections in humans and animals.

Continuous N-Hydroxyphthalimide (NHPI)-Mediated Electrochemical Aerobic Oxidation of Benzylic C-H Bonds.

Electroorganic chemistry has emerged as an environmentally benign tool for synthetic chemists to achieve efficient transformations that are challenging with traditional reagent-based methods. Continuous flow chemistry brings pharmaceutical industry numerous advantages, but implementing electroorganic synthesis in flow is challenging, especially for electroorganic reactions with coupled electrode reactions and slow chemical reactions. We present a continuous electrolysis system engineered for N-hydroxyphthalimide (NHPI) mediated electrochemical aerobic oxidation of benzylic C-H bonds. First, a cation-exchange membrane prevents the crossover of the NHPI anion from anolyte to catholyte avoiding reductive decomposition of NHPI at the cathode, and enables the usage of a cost-effective reticulated vitreous carbon (RVC) cathode instead of a platinum electrode. Second, running the electrochemical flow cell with recycle streams accommodates the inherently slow kinetics of the chemical reaction without phthalimide-N-oxyl (PINO) radical self-decomposition at the anode, and allows the usage of gaseous oxygen as co-oxidant.

Identification and characterization of a periplasmic trilactone esterase, Cee, revealed unique features of ferric enterobactin acquisition in Campylobacter.

Ferric enterobactin (FeEnt) acquisition is a highly efficient and conserved iron scavenging system in Gram-negative bacteria. Recently, we have characterized two FeEnt receptors (CfrA and CfrB) in Campylobacter jejuni and C. coli, the enteric human pathogens that do not produce any siderophores. In this study, whole-genome sequencing and comparative genomic analysis identified a unique Ent trilactone esterase Cee (Cj1376) in C. jejuni. Genomic analysis and biochemical assay strongly suggested that Cee is the sole trilactone esterase in C. jejuni. Thin-layer chromatography and HPLC analyses showed high efficiency of the purified Cee to hydrolyse Ent. Three Cee homologues previously characterized from other bacteria (IroE, IroD and Fes) were also purified and analysed together with Cee, indicating that Cee, Fes and IroD displayed similar hydrolysis dynamics for both apo and ferric forms of Ent while IroE catalysed Ent inefficiently. Unlike cytoplasmic Fes and IroD, Cee is localized in the periplasm as demonstrated by immunoblotting using Cee-specific antibodies. Genetic manipulation of diverse Campylobacter strains demonstrated that Cee is not only essential for CfrB-dependent FeEnt acquisition but also involved in CfrA-dependent pathway. Together, this study identified and characterized a novel periplasmic trilactone esterase and suggested a new model of FeEnt acquisition in Campylobacter.

Identification and characterization of a bile salt hydrolase from Lactobacillus salivarius for development of novel alternatives to antibiotic growth promoters.

Antibiotic growth promoters (AGPs) have been used as feed additives to improve average body weight gain and feed efficiency in food animals for more than 5 decades. However, there is a worldwide trend to limit AGP use to protect food safety and public health, which raises an urgent need to discover effective alternatives to AGPs. The growth-promoting effect of AGPs has been shown to be highly correlated with the decreased activity of intestinal bile salt hydrolase (BSH), an enzyme that is produced by various gut microflora and involved in host lipid metabolism. Thus, BSH inhibitors are likely promising feed additives to AGPs to improve animal growth performance. In this study, the genome of Lactobacillus salivarius NRRL B-30514, a BSH-producing strain isolated from chicken, was sequenced by a 454 GS FLX sequencer. A BSH gene identified by genome analysis was cloned and expressed in an Escherichia coli expression system for enzymatic analyses. The BSH displayed efficient hydrolysis activity for both glycoconjugated and tauroconjugated bile salts, with slightly higher catalytic efficiencies (k(cat)/K(m)) on glycoconjugated bile salts. The optimal pH and temperature for the BSH activity were 5.5 and 41°C, respectively. Examination of a panel of dietary compounds using the purified BSH identified some potent BSH inhibitors, in which copper and zinc have been recently demonstrated to promote feed digestion and body weight gain in different food animals. In sum, this study identified and characterized a BSH with broad substrate specificity from a chicken L. salivarius strain and established a solid platform for us to discover novel BSH inhibitors, the promising feed additives to replace AGPs for enhancing the productivity and sustainability of food animals.

Detergent-associated solution conformations of helical and beta-barrel membrane proteins.

Membrane proteins present major challenges for structural biology. In particular, the production of suitable crystals for high-resolution structural determination continues to be a significant roadblock for developing an atomic-level understanding of these vital cellular systems. The use of detergents for extracting membrane proteins from the native membrane for either crystallization or reconstitution into model lipid membranes for further study is assumed to leave the protein with the proper fold with a belt of detergent encompassing the membrane-spanning segments of the structure. Small-angle X-ray scattering was used to probe the detergent-associated solution conformations of three membrane proteins, namely bacteriorhodopsin (BR), the Ste2p G-protein coupled receptor from Saccharomyces cerevisiae, and the Escherichia coli porin OmpF. The results demonstrate that, contrary to the traditional model of a detergent-associated membrane protein, the helical proteins BR and Ste2p are not in the expected, compact conformation and associated with detergent micelles, while the beta-barrel OmpF is indeed embedded in a disk-like micelle in a properly folded state. The comparison provided by the BR and Ste2p, both members of the 7TM family of helical membrane proteins, further suggests that the interhelical interactions between the transmembrane helices of the two proteins differ, such that BR, like other rhodopsins, can properly refold to crystallize, while Ste2p continues to prove resistant to crystallization from an initially detergent-associated state.

Analysis of RF heating and sample stability in aligned static solid-state NMR spectroscopy.

Sample instability during solid-state NMR experiments frequently arises due to RF heating in aligned samples of hydrated lipid bilayers. A new, simple approach for estimating sample temperature is used to show that, at 9.4 T, sample heating depends mostly on (1)H decoupling power rather than on (15)N irradiation in PISEMA experiments. Such heating for different sample preparations, including lipid composition, salt concentration and hydration level was assessed and the hydration level was found to be the primary parameter correlated with sample heating. The contribution to RF heating from the dielectric loss appears to be dominant under our experimental conditions. The heat generated by a single scan was approximately calculated from the Q values of the probe, to be a 1.7 degrees C elevation per single pulse sequence iteration under typical sample conditions. The steady-state sample temperature during PISEMA experiments can be estimated based on the method presented here, which correlates the loss factor with the temperature rise induced by the RF heating of the sample.