Screening potential dye sensitizers for water splitting photocatalysts using a genetic algorithm.

Addressing the global fossil energy crisis necessitates the efficient utilization of sustainable energy sources. Hydrogen, a green fuel, can be generated using sunlight, water, and a photocatalyst. Employing sensitizers holds promise for enhancing photocatalyst performance, enabling high rates of hydrogen evolution through increased visible light absorption. However, sifting through millions of diverse molecules to identify suitable dyes for specific photocatalysts poses a significant challenge. In this study, we integrate genetic algorithm and geometry-frequency-noncovalent extended tight binding methods to efficiently screen 2.6 million potential sensitizers with a D-π-A-π-AA structure within a short timeframe. Subsequently, these optimized sensitizers are rigorously reassessed by using DFT/TDDFT methods, elucidating why they may serve as superior dyes compared to the reference dye WS5F, particularly in terms of light absorption, driving force, binding energy, etc. Additionally, our methodology uncovers molecular motifs of particular interest, including the furan π-bridge and the double cyano anchoring acceptor, which are prevalent in the most promising set of molecules. The developed genetic algorithm workflow and dye design principles can be extended to various compelling projects, such as dye-sensitized solar cells, organic photovoltaics, photo-induced redox reactions, pharmaceuticals, and beyond.

Sequential closed-loop Bayesian optimization as a guide for organic molecular metallophotocatalyst formulation discovery.

Conjugated organic photoredox catalysts (OPCs) can promote a wide range of chemical transformations. It is challenging to predict the catalytic activities of OPCs from first principles, either by expert knowledge or by using a priori calculations, as catalyst activity depends on a complex range of interrelated properties. Organic photocatalysts and other catalyst systems have often been discovered by a mixture of design and trial and error. Here we report a two-step data-driven approach to the targeted synthesis of OPCs and the subsequent reaction optimization for metallophotocatalysis, demonstrated for decarboxylative sp3-sp2 cross-coupling of amino acids with aryl halides. Our approach uses a Bayesian optimization strategy coupled with encoding of key physical properties using molecular descriptors to identify promising OPCs from a virtual library of 560 candidate molecules. This led to OPC formulations that are competitive with iridium catalysts by exploring just 2.4% of the available catalyst formulation space (107 of 4,500 possible reaction conditions).

Parametric interaction of laser cavity-solitons with an external CW pump.

We study the interaction of a laser cavity-soliton microcomb with an externally coupled, co-propagating tunable CW pump, observing parametric Kerr interactions which lead to the formation of both a cross-phase modulation and a four-wave mixing replica of the laser cavity-soliton. We compare and explain the dependence of the microcomb spectra from both the cavity-soliton and pump parameters, demonstrating the ability to adjust the microcomb externally without breaking or interfering with the soliton state. The parametric nature of the process agrees with numerical simulations. The parametric extended state maintains the typical robustness of laser-cavity solitons.

Self-Assembly of Chiral Porous Metal-Organic Polyhedra from Trianglsalen Macrocycles.

Metal-organic polyhedra (MOPs) can exhibit tunable porosity and functionality, suggesting potential for applications such as molecular separations. MOPs are typically constructed by the bottom-up multicomponent self-assembly of organic ligands and metal ions, and the final functionality can be hard to program. Here, we used trianglsalen macrocycles as preorganized building blocks to assemble octahedral-shaped MOPs. The resultant MOPs inherit most of the preorganized properties of the macrocyclic ligands, including their well-defined cavities and chirality. As a result, the porosity in the MOPs could be tuned by modifying the structure of the macrocycle building blocks. Using this strategy, we could systematically enlarge the size of the MOPs from 26.3 to 32.1 Å by increasing the macrocycle size. The family of MOPs shows experimental surface areas of up to 820 m2/g, and they are stable in water. One of these MOPs can efficiently separate the rare gases Xe from Kr because the prefabricated macrocyclic windows of MOPs can be modified to sit at the Xe/Kr size cutoff range.

Porous isoreticular non-metal organic frameworks.

Metal-organic frameworks (MOFs) are useful synthetic materials that are built by the programmed assembly of metal nodes and organic linkers1. The success of MOFs results from the isoreticular principle2, which allows families of structurally analogous frameworks to be built in a predictable way. This relies on directional coordinate covalent bonding to define the framework geometry. However, isoreticular strategies do not translate to other common crystalline solids, such as organic salts3-5, in which the intermolecular ionic bonding is less directional. Here we show that chemical knowledge can be combined with computational crystal-structure prediction6 (CSP) to design porous organic ammonium halide salts that contain no metals. The nodes in these salt frameworks are tightly packed ionic clusters that direct the materials to crystallize in specific ways, as demonstrated by the presence of well-defined spikes of low-energy, low-density isoreticular structures on the predicted lattice energy landscapes7,8. These energy landscapes allow us to select combinations of cations and anions that will form thermodynamically stable, porous salt frameworks with channel sizes, functionalities and geometries that can be predicted a priori. Some of these porous salts adsorb molecular guests such as iodine in quantities that exceed those of most MOFs, and this could be useful for applications such as radio-iodine capture9-12. More generally, the synthesis of these salts is scalable, involving simple acid-base neutralization, and the strategy makes it possible to create a family of non-metal organic frameworks that combine high ionic charge density with permanent porosity.

Conjugated Polymer/Recombinant Escherichia coli Biohybrid Systems for Photobiocatalytic Hydrogen Production.

Biohybrid photocatalysts are composite materials that combine the efficient light-absorbing properties of synthetic materials with the highly evolved metabolic pathways and self-repair mechanisms of biological systems. Here, we show the potential of conjugated polymers as photosensitizers in biohybrid systems by combining a series of polymer nanoparticles with engineered Escherichia coli cells. Under simulated solar light irradiation, the biohybrid system consisting of fluorene/dibenzo [b,d]thiophene sulfone copolymer (LP41) and recombinant E. coli (i.e., a LP41/HydA BL21 biohybrid) shows a sacrificial hydrogen evolution rate of 3.442 mmol g-1 h-1 (normalized to polymer amount). It is over 30 times higher than the polymer photocatalyst alone (0.105 mmol g-1 h-1), while no detectable hydrogen was generated from the E. coli cells alone, demonstrating the strong synergy between the polymer nanoparticles and bacterial cells. The differences in the physical interactions between synthetic materials and microorganisms, as well as redox energy level alignment, elucidate the trends in photochemical activity. Our results suggest that organic semiconductors may offer advantages, such as solution processability, low toxicity, and more tunable surface interactions with the biological components over inorganic materials.

Gas Uptake and Thermodynamics in Porous Liquids Elucidated by 129Xe NMR.

We exploited 129Xe NMR to investigate xenon gas uptake and dynamics in a porous liquid formed by dissolving porous organic cages in a cavity-excluded solvent. Quantitative 129Xe NMR shows that when the amount of xenon added to the sample is lower than the amount of cages present (subsaturation), the porous liquid absorbs almost all xenon atoms from the gas phase, with 30% of the cages occupied with a Xe atom. A simple two-site exchange model enables an estimate of the chemical shift of 129Xe in the cages, which is in good agreement with the value provided by first-principles modeling. T2 relaxation times allow the determination of the exchange rate of Xe between the solvent and cage sites as well as the activation energies of the exchange. The 129Xe NMR analysis also enables determination of the free energy of confinement, and it shows that Xe binding is predominantly enthalpy-driven.

Deep sequencing of Phox2a nuclei reveals five classes of anterolateral system neurons.

The anterolateral system (ALS) is a major ascending pathway from the spinal cord that projects to multiple brain areas and underlies the perception of pain, itch, and skin temperature. Despite its importance, our understanding of this system has been hampered by the considerable functional and molecular diversity of its constituent cells. Here, we use fluorescence-activated cell sorting to isolate ALS neurons belonging to the Phox2a-lineage for single-nucleus RNA sequencing. We reveal five distinct clusters of ALS neurons (ALS1-5) and document their laminar distribution in the spinal cord using in situ hybridization. We identify three clusters of neurons located predominantly in laminae I-III of the dorsal horn (ALS1-3) and two clusters with cell bodies located in deeper laminae (ALS4 and ALS5). Our findings reveal the transcriptional logic that underlies ALS neuronal diversity in the adult mouse and uncover the molecular identity of two previously identified classes of projection neurons. We also show that these molecular signatures can be used to target groups of ALS neurons using retrograde viral tracing. Overall, our findings provide a valuable resource for studying somatosensory biology and targeting subclasses of ALS neurons.

2D to 3D Reconstruction of Boron-Linked Covalent-Organic Frameworks.

The transformation of two-dimensional (2D) covalent-organic frameworks (COFs) into three-dimensions (3D) is synthetically challenging, and it is typically addressed through interlayer cross-linking of alkene or alkyne bonds. Here, we report the first example of the chemical reconstruction of a 2D COF to a 3D COF with a complete lattice rearrangement facilitated by base-triggered boron hybridization. This chemical reconstruction involves the conversion of trigonal boronate ester linkages to tetrahedral anionic spiroborate linkages. This transformation reticulates the coplanar, closely stacked square cobalt(II) phthalocyanine (PcCo) units into a 3D perpendicular arrangement. As a result, the pore size of COFs expands from 2.45 nm for the initial 2D square lattice (sql) to 3.02 nm in the 3D noninterpenetrated network (nbo). Mechanistic studies reveal a base-catalyzed boronate ester protodeboronation pathway for the formation of the spiroborate structure.

A soft cable loop based gripper for robotic automation of chemistry.

Robotic automation is proving itself indispensable in the modern Chemistry laboratory, but adoption is slowed down by the technical challenges of implementing such systems. This paper reports on a novel adaptive gripper mechanism that can easily and reliably grasp cylindrical and prismatic objects of various sizes with limited clearance required. The proposed design exploits the inherent compliance of a cable that is driven to fully envelope the target object. The cable is run through a rigid finger, allowing the loop to be placed around objects with minimal clearance required and to provide support for the object once the grip is complete. Thanks to the compliant nature of the mechanism, the gripper requires minimal control effort to complete a gasping task. A prototype of the gripper has been designed and built for chemistry automation tasks, where it showed very high grasp reliability with ≤ 1 % grasp failures.

Mixed-Linker Strategy for the Construction of Sulfone-Containing D-A-A Covalent Organic Frameworks for Efficient Photocatalytic Hydrogen Peroxide Production.

The solar-driven photocatalytic production of hydrogen peroxide (H2O2) from water and oxygen using semiconductor catalysts offers a promising approach for converting solar energy into storable chemical energy. However, the efficiency of photocatalytic H2O2 production is often restricted by the low photo-generated charge separation, slow surface reactions and inadequate stability. Here, we developed a mixed-linker strategy to build a donor-acceptor-acceptor (D-A-A) type covalent organic framework (COF) photocatalyst, FS-OHOMe-COF. The FS-OHOMe-COF structure features extended π-π conjugation that improves charge mobility, while the introduction of sulfone units not only as active sites facilitates surface reactions with water but also bolsters stability through increased interlayer forces. The resulting FS-OHOMe-COF has a low exciton binding energy, long excited-state lifetime and high photo-stability that leads to high performance for photocatalytic H2O2 production (up to 1.0 mM h-1) with an H2O2 output of 19 mM after 72 hours of irradiation. Furthermore, the catalyst demonstrates high stability, which sustained activity over 192 hours of photocatalytic experiment.

Modular, multi-robot integration of laboratories: an autonomous workflow for solid-state chemistry.

Automation can transform productivity in research activities that use liquid handling, such as organic synthesis, but it has made less impact in materials laboratories, which require sample preparation steps and a range of solid-state characterization techniques. For example, powder X-ray diffraction (PXRD) is a key method in materials and pharmaceutical chemistry, but its end-to-end automation is challenging because it involves solid powder handling and sample processing. Here we present a fully autonomous solid-state workflow for PXRD experiments that can match or even surpass manual data quality, encompassing crystal growth, sample preparation, and automated data capture. The workflow involves 12 steps performed by a team of three multipurpose robots, illustrating the power of flexible, modular automation to integrate complex, multitask laboratories.

Fine-tuning GPT-3 for machine learning electronic and functional properties of organic molecules.

We evaluate the effectiveness of fine-tuning GPT-3 for the prediction of electronic and functional properties of organic molecules. Our findings show that fine-tuned GPT-3 can successfully identify and distinguish between chemically meaningful patterns, and discern subtle differences among them, exhibiting robust predictive performance for the prediction of molecular properties. We focus on assessing the fine-tuned models' resilience to information loss, resulting from the absence of atoms or chemical groups, and to noise that we introduce via random alterations in atomic identities. We discuss the challenges and limitations inherent to the use of GPT-3 in molecular machine-learning tasks and suggest potential directions for future research and improvements to address these issues.

Association Between ICD-10 Codes for Social Needs and Subsequent Emergency and Inpatient Use.

BACKGROUND: International Classification of Diseases, 10th revision Z codes capture social needs related to health care encounters and may identify elevated risk of acute care use. OBJECTIVES: To examine associations between Z code assignment and subsequent acute care use and explore associations between social need category and acute care use. RESEARCH DESIGN: Retrospective cohort study. SUBJECTS: Adults continuously enrolled in a commercial or Medicare Advantage plan for ≥15 months (12-month baseline, 3-48 month follow-up). OUTCOMES: All-cause emergency department (ED) visits and inpatient admissions during study follow-up. RESULTS: There were 352,280 patients with any assigned Z codes and 704,560 sampled controls with no Z codes. Among patients with commercial plans, Z code assignment was associated with a 26% higher rate of ED visits [adjusted incidence rate ratio (aIRR) 1.26, 95% CI: 1.25-1.27] and 42% higher rate of inpatient admissions (aIRR 1.42, 95% CI: 1.39-1.44) during follow-up. Among patients with Medicare Advantage plans, Z code assignment was associated with 42% (aIRR 1.42, 95% CI: 1.40-1.43) and 28% (aIRR 1.28, 95% CI: 1.26-1.30) higher rates of ED visits and inpatient admissions, respectively. Within the Z code group, relative to community/social codes, socioeconomic Z codes were associated with higher rates of inpatient admissions (commercial: aIRR 1.10, 95% CI: 1.06-1.14; Medicare Advantage: aIRR 1.24, 95% CI 1.20-1.27), and environmental Z codes were associated with lower rates of both primary outcomes. CONCLUSIONS: Z code assignment was independently associated with higher subsequent emergency and inpatient utilization. Findings suggest Z codes' potential utility for risk prediction and efforts targeting avoidable utilization.

Machine Learning Accelerated Exploration of Ternary Organic Heterojunction Photocatalysts for Sacrificial Hydrogen Evolution.

Donor-acceptor heterojunctions in organic photocatalysts can provide enhanced exciton dissociation and charge separation, thereby improving the photocatalytic activity. However, the wide choice of possible donors and acceptors poses a challenge for the rational design of organic heterojunction photocatalysts, particularly for large ternary phase spaces. We accelerated the exploration of ternary organic heterojunction photocatalysts (TOHP) by using a combination of machine learning and high-throughput experimental screening. This involved 736 experiments in all, out of possible 4320 ternary combinations. The top ten most active TOHPs discovered using this strategy showed outstanding sacrificial hydrogen production rates of more than 500 mmol g-1 h-1, with the most active ternary material reaching a rate of 749.8 mmol g-1 h-1 under 1 sun illumination. These rates of photocatalytic hydrogen generation are among the highest reported for organic photocatalysts in the literature.

Association of primary care visit length with outpatient utilization, continuity, and care processes.

BACKGROUND: Little is known about how variation in the scheduled length of primary care visits can impact patients' patterns of health care utilization. OBJECTIVE: To evaluate how the scheduled length of in-person visits with primary care physicians (PCPs) was associated with PCP and patient characteristics, outpatient utilization, and preventive care receipt. METHODS: This retrospective cohort study examined data from a large American academic health system. PCP visit length template was defined as either 15- and 30-min scheduled appointments (i.e. 15/30), or 20- and 40-min scheduled appointments (i.e. 20/40). RESULTS: Of 222 included PCPs, 85 (38.3%) used the 15/30 template and 137 (61.7%) used the 20/40 template. The 15/30 group had higher proportions of male (49.4%, vs. 35.8% in the 20/40 group) and family medicine (37.6% vs. 21.2%) physicians. In adjusted patient-level analysis (N = 238,806), having a 15/30 PCP was associated with 9% more primary care visits (incidence rate ratio [IRR], 1.09; 95% confidence interval [CI], 1.03-1.14), and 8% fewer specialty care visits (IRR, 0.92; 95% CI, 0.86-0.98). PCP visit length template was not associated with significant differences in obstetrics/gynaecology visits, continuity of care, or preventive care receipt. In interaction analyses, having a 15/30 PCP was associated with additional primary care visits among non-Hispanic White patients (IRR, 1.10; 95% CI, 1.04-1.16) but not among non-Hispanic Black patients. CONCLUSION: PCPs' choices about the scheduled length of in-person visits may impact their patients' specialty care use, and have varying impacts across different racial/ethnic groups.

Go with the flow: deep learning methods for autonomous viscosity estimations.

Closed-loop experiments can accelerate material discovery by automating both experimental manipulations and decisions that have traditionally been made by researchers. Fast and non-invasive measurements are particularly attractive for closed-loop strategies. Viscosity is a physical property for fluids that is important in many applications. It is fundamental in application areas such as coatings; also, even if viscosity is not the key property of interest, it can impact our ability to do closed-loop experimentation. For example, unexpected increases in viscosity can cause liquid-handling robots to fail. Traditional viscosity measurements are manual, invasive, and slow. Here we use convolutional neural networks (CNNs) as an alternative to traditional viscometry by non-invasively extracting the spatiotemporal features of fluid motion under flow. To do this, we built a workflow using a dual-armed collaborative robot that collects video data of fluid motion autonomously. This dataset was then used to train a 3-dimensional convolutional neural network (3D-CNN) for viscosity estimation, either by classification or by regression. We also used these models to identify unknown laboratory solvents, again based on differences in fluid motion. The 3D-CNN model performance was compared with the performance of a panel of human participants for the same classification tasks. Our models strongly outperformed human classification in both cases. For example, even with training on fewer than 50 videos for each liquid, the 3D-CNN model gave an average accuracy of 88% for predicting the identity of five different laboratory solvents, compared to an average accuracy of 32% for human observation. For comparison, random category selection would give an average accuracy of 20%. Our method offers an alternative to traditional viscosity measurements for autonomous chemistry workflows that might be used both for process control (e.g., choosing not to pipette liquids that are too viscous) or for materials discovery (e.g., identifying new polymerization catalysts on the basis of viscosification).

Making the connections: physical and electric interactions in biohybrid photosynthetic systems.

Biohybrid photosynthesis systems, which combine biological and non-biological materials, have attracted recent interest in solar-to-chemical energy conversion. However, the solar efficiencies of such systems remain low, despite advances in both artificial photosynthesis and synthetic biology. Here we discuss the potential of conjugated organic materials as photosensitisers for biological hybrid systems compared to traditional inorganic semiconductors. Organic materials offer the ability to tune both photophysical properties and the specific physicochemical interactions between the photosensitiser and biological cells, thus improving stability and charge transfer. We highlight the state-of-the-art and opportunities for new approaches in designing new biohybrid systems. This perspective also summarises the current understanding of the underlying electron transport process and highlights the research areas that need to be pursued to underpin the development of hybrid photosynthesis systems.