In situ formed Se-TiO as a highly reusable photocatalyst for selenium reduction and removal from industrial wastewater.

Selenium (Se) release from anthropogenic activities such as mining, power generation, and agriculture poses considerable environmental and ecological risks. Increasing prevalence and awareness of Se-related issues have driven the development of many innovative Se treatment technologies. Photocatalysis has shown promise towards Se removal from industrial wastewaters with minimal residuals, and is generally considered a low-cost, robust, non-toxic, and potentially solar-powered method.

Preventing radiation and chemo toxicity: Insights from bone marrow-on-a-chip.

Species-specific differences motivate the development of human hematopoiesis models. Georgescu et al. present a microfluidic model of the human bone marrow perivascular niche to capture innate immune cell mobilization.

Active Probing of a RuO/CZ Catalyst Surface as a Tool for Bridging the Gap Between CO Oxidation Catalytic Tests in a Model and Realistic Exhaust Gas Stream.

Herein, we present a paper that attempts to bridge the gap between CO oxidation catalytic tests performed in a model stream and a more realistic exhaust gas stream by incorporating characterization methods that allow for active probing of the catalyst surface. The results have shown that it is not just the abundance of a given type of species on the surface that impacts the activity of a system but also the ease of extraction of ions from their surface (time-of-flight secondary ion mass spectrometry) and the response of the support to change in the feed composition (dynamic in situ X-ray diffraction (XRD) with variable atmosphere). The study utilizes the method of doping a catalyst (RuO/CZ) with a small amount of alkali-metal (K or Na) carbonates in order to slightly modify its surface to gain insight into parameters that may cause discrepancies between model stream activity and complex stream activity.

Ambient solar-driven selenate reduction and removal from industrial brine using TiO-based buoyant photocatalysts.

Selenium (Se), released from mining, power generation, and agriculture, is an environmentally and ecologically concerning contaminant due to its toxicity at elevated concentrations. Se oxyanions are highly soluble and mobile in aquatic ecosystems, and have a strong tendency to bioaccumulate and biomagnify, leading to acute and chronic toxicity in animals and humans. Photocatalysis presents a promising sustainable Se treatment solution and has successfully reduced and removed Se from mining-influenced matrices using UV-powered slurry photoreactor systems.

Automated electrosynthesis reaction mining with multimodal large language models (MLLMs).

Leveraging the chemical data available in legacy formats such as publications and patents is a significant challenge for the community. Automated reaction mining offers a promising solution to unleash this knowledge into a learnable digital form and therefore help expedite materials and reaction discovery. However, existing reaction mining toolkits are limited to single input modalities (text or images) and cannot effectively integrate heterogeneous data that is scattered across text, tables, and figures.

Colloidal Hydrogel with Staged Sequestration and Release of Molecules Undergoing Competitive Binding.

Competitive binding of distinct molecules in the hydrogel interior can facilitate dynamic exchange between the hydrogel and the surrounding environment. The ability to control the rates of sequestration and release of these molecules would enhance the hydrogel's functionality and enable targeting of a specific task. Here, we report the design of a colloidal hydrogel with two distinct pore dimensions to achieve staged, diffusion-controlled scavenging and release dynamics of molecules undergoing competitive binding.

Applications and evolution of 3D cancer-immune cell models.

Understanding the highly complex tumor-immune landscape is an important goal for developing novel immune therapies for solid cancers. To this end, 3D cancer-immune models have emerged as patient-relevant in vitro tools for modeling the tumor-immune landscape and the cellular interactions within it. In this review, we provide an overview of the components and applications of 3D cancer-immune models and discuss their evolution from 2015 to 2023.

Structure analysis of the telomere resolvase from the Lyme disease spirochete Borrelia garinii reveals functional divergence of its C-terminal domain.

Borrelia spirochetes are the causative agents of Lyme disease and relapsing fever, two of the most common tick-borne illnesses. A characteristic feature of these spirochetes is their highly segmented genomes which consists of a linear chromosome and a mixture of up to approximately 24 linear and circular extrachromosomal plasmids. The complexity of this genomic arrangement requires multiple strategies for efficient replication and partitioning during cell division, including the generation of hairpin ends found on linear replicons mediated by the essential enzyme ResT, a telomere resolvase.

A new 3D printing strategy by enhancing shear-induced alignment of gelled nanomaterial inks resulting in stronger and ductile cellulose films.

Cellulose nanofibrils (CNFs) are derived from biomass and have significant potential as fossil-based plastic alternatives used in disposable electronics. Controlling the nanostructure of fibrils is the key to obtaining strong mechanical properties and high optical transparency. Vacuum filtration is usually used to prepare the CNFs film in the literature; however, such a process cannot control the structure of the CNFs film, which limits the transparency and mechanical strength of the film.

In Silico Chemical Experiments in the Age of AI: From Quantum Chemistry to Machine Learning and Back.

Computational chemistry is an indispensable tool for understanding molecules and predicting chemical properties. However, traditional computational methods face significant challenges due to the difficulty of solving the Schrödinger equations and the increasing computational cost with the size of the molecular system. In response, there has been a surge of interest in leveraging artificial intelligence (AI) and machine learning (ML) techniques to in silico experiments.

Computational Investigations of the Detailed Mechanism of Reverse Intersystem Crossing in Inverted Singlet-Triplet Gap Molecules.

Inverted singlet-triplet gap (INVEST) materials have promising photophysical properties for optoelectronic applications due to an inversion of their lowest singlet (S) and triplet (T) excited states. This results in an exothermic reverse intersystem crossing (rISC) process that potentially enhances triplet harvesting, compared to thermally activated delayed fluorescence (TADF) emitters with endothermic rISCs. However, the processes and phenomena that facilitate conversion between excited states for INVEST materials are underexplored.

Artificial Intelligence for Retrosynthetic Planning Needs Both Data and Expert Knowledge.

Rapid advancements in artificial intelligence (AI) have enabled breakthroughs across many scientific disciplines. In organic chemistry, the challenge of planning complex multistep chemical syntheses should conceptually be well-suited for AI. Yet, the development of AI synthesis planners trained solely on reaction-example-data has stagnated and is not on par with the performance of "hybrid" algorithms combining AI with expert knowledge.

DELFI: a computer oracle for recommending density functionals for excited states calculations.

Density functional theory (DFT) is the workhorse of computational quantum chemistry. One of its main limitations is that choosing the right functional is a non-trivial task left for human experts. The choice is particularly hard for excited state calculations when using its time-dependent formulation (TD-DFT).

Leave No Photon Behind: Artificial Intelligence in Multiscale Physics of Photocatalyst and Photoreactor Design.

Although solar fuels photocatalysis offers the promise of converting carbon dioxide directly with sunlight as commercially scalable solutions have remained elusive over the past few decades, despite significant advancements in photocatalysis band-gap engineering and atomic site activity. The primary challenge lies not in the discovery of new catalyst materials, which are abundant, but in overcoming the bottlenecks related to material-photoreactor synergy. These factors include achieving photogeneration and charge-carrier recombination at reactive sites, utilizing high mass transfer efficiency supports, maximizing solar collection, and achieving uniform light distribution within a reactor.

Artificial design of organic emitters a genetic algorithm enhanced by a deep neural network.

The design of molecules requires multi-objective optimizations in high-dimensional chemical space with often conflicting target properties. To navigate this space, classical workflows rely on the domain knowledge and creativity of human experts, which can be the bottleneck in high-throughput approaches. Herein, we present an artificial molecular design workflow relying on a genetic algorithm and a deep neural network to find a new family of organic emitters with inverted singlet-triplet gaps and appreciable fluorescence rates.

Simultaneous secondary electron microscopy in the scanning transmission electron microscope with applications for in situ studies.

Scanning/transmission electron microscopy (STEM) is a powerful characterization tool for a wide range of materials. Over the years, STEMs have been extensively used for in situ studies of structural evolution and dynamic processes. A limited number of STEM instruments are equipped with a secondary electron (SE) detector in addition to the conventional transmitted electron detectors, i.

In-situ TEM study from the perspective of holders.

During the in situ transmission electron microscopy (TEM) observations, the diverse functionalities of different specimen holders play a crucial role. We hereby provide a comprehensive overview of the main types of holders, associated technologies and case studies pertaining to the widely employed heating and gas heating methods, from their initial developments to the latest advancement. In addition to the conventional approaches, we also discuss the emergence of holders that incorporate a micro-electro-mechanical system (MEMS) chip for in situ observations.

Advances in generative modeling methods and datasets to design novel enzymes for renewable chemicals and fuels.

Biotechnology has revolutionized the development of sustainable energy sources by harnessing biomass as a feedstock for energy production. However, challenges such as recalcitrant feedstocks and inefficient metabolic pathways hinder the large-scale integration of renewable energy systems. Enzyme engineering has emerged as a powerful tool to address these challenges by enhancing enzyme activity, specificity, and stability.

Exploring New Dimensions of Tumor Heterogeneity: The Application of Single Cell Analysis to Organoid-Based 3D In Vitro Models.

Modeling the heterogeneity of the tumor microenvironment (TME) in vitro is essential to investigating fundamental cancer biology and developing novel treatment strategies that holistically address the factors affecting tumor progression and therapeutic response. Thus, the development of new tools for both in vitro modeling, such as patient-derived organoids (PDOs) and complex 3D in vitro models, and single cell omics analysis, such as single-cell RNA-sequencing, represents a new frontier for investigating tumor heterogeneity. Specifically, the integration of PDO-based 3D in vitro models and single cell analysis offers a unique opportunity to explore the intersecting effects of interpatient, microenvironmental, and tumor cell heterogeneity on cell phenotypes in the TME.

Augmenting Polymer Datasets by Iterative Rearrangement.

One of the biggest obstacles to successful polymer property prediction is an effective representation that accurately captures the sequence of repeat units in a polymer. Motivated by the success of data augmentation in computer vision and natural language processing, we explore augmenting polymer data by iteratively rearranging the molecular representation while preserving the correct connectivity, revealing additional substructural information that is not present in a single representation. We evaluate the effects of this technique on the performance of machine learning models trained on three polymer datasets and compare them to common molecular representations.

Peltier cooling for the reduction of carbon contamination in scanning electron microscopy.

We used a novel Peltier anticontamination device (PAC) to reduce carbon contamination upon electron beam irradiation in scanning electron microscopy through a reduction of hydrocarbon molecules in the specimen chamber. Unlike liquid-nitrogen based cold traps, the PAC operates free of user maintenance and is suitable for lengthy imaging sessions without degradation of the anticontamination performance. Its performance as an alternative cold trap method provides considerable reduction of electron beam-assisted carbon build-up.

Dual-Function Hydrogel Dressings with a Dynamic Exchange of Iron Ions and an Antibiotic Drug for Treatment of Infected Wounds.

Bacterial infection is a major problem with diabetic wounds that may result in nonhealing chronic ulcers. Here, we report an approach to antibacterial hydrogel dressings for enhanced treatment of infected skin wounds. A fibrous hydrogel was derived from cellulose nanocrystals that were modified with dopamine and cross-linked with gelatin.

Electrospun Silica-Polyacrylonitrile Nanohybrids for Water Treatments.

In this work, the removal of NOM (natural organic matter) as represented by humic acid by means of electrospun nanofiber adsorptive membranes (ENAMs) is described. Polyacrylonitrile (PAN) was used for the preparation of ENAMs incorporating silica nanoparticles as adsorbents. The addition of silica to the polymer left visible changes on the structural morphology and fibers' properties of the membrane.

Guided discovery of chemical reaction pathways with imposed activation.

Computational power and quantum chemical methods have improved immensely since computers were first applied to the study of reactivity, but the prediction of chemical reactions has remained challenging. We show that complex reaction pathways can be efficiently predicted in a guided manner using chemical activation imposed by geometrical constraints of specific reactive modes, which we term imposed activation (IACTA). Our approach is demonstrated on realistic and challenging chemistry, such as a triple cyclization cascade involved in the total synthesis of a natural product, a water-mediated Michael addition, and several oxidative addition reactions of complex drug-like molecules.