High-Throughput Synthesis and Screening of Titania-Based Photocatalysts

Application of Low-Temperature Air Plasma for the Enhancement of Defense Fabric's Self-Cleaning Property

Self-cleaning textiles have the potential to revolutionize the lives of people like military personnel and hikers who spend extended periods of time in the sun and have restricted access to washing facilities. This research aims to develop the self-cleaning capability of defense uniforms by utilizing air plasma treatment and applying TiO2 nanocoating. Following plasma treatment of differing durations (2, 4, 6, 8, and 10 min, respectively), a pad-dry cure method was employed to apply a TiO2 coating to each sample, while keeping other processing parameters constant. SEM, Fourier transform infrared spectroscopy, ultraviolet protection factor (UVPF), energy-dispersive X-rays, and a water contact angle test were performed in order to validate the air plasma-induced surface modification. There was a gradual escalation in the rate of TiO2 absorption with an extension of the plasma treatment duration. Afterward, the samples were stained with various organic and inorganic compounds, including oil, ink, soil, and coffee, and subsequently exposed to sunlight for a period of 6 h. The samples demonstrated an enhanced cleaning effectiveness with increasing quantities of TiO2. The reflectance value and visual assessment of washed sample showed a reduced yet still present self-cleaning characteristic. The UVPF of the samples increased gradually as the duration of plasma treatment increased due to the UV absorption properties of TiO2, as validated by measuring the band gap energy.

High-throughput experimentation for photocatalytic water purification in practical environments

High-throughput screening instrument was developed for photocatalytic water purification, enabling the simultaneous testing of 132 photocatalytic reactions under uniform visible light irradiation, temperature control, and stirring. The instrument was used to investigate the effects of different catalysts (TiO2, ZnO, α-Fe2O3) and environmental waters (seawater, urban wastewater, and industrial wastewater) on dye degradation. It was observed environmental ions, particularly carbonate and phosphate ions, significantly reduced catalyst activity by inhibiting the adsorption of dye molecules. To develop effective catalysts for dye degradation in industrial wastewater, 15 types of noble metal nanoparticles (NPs) were supported on photocatalysts. The study found that noble metal NPs with high work functions and oxidation resistance, such as Au and Pt, exhibited higher activity even in the industrial wastewater, likely converting environmental ions into active species. These findings, based on 432 test results, demonstrate the effectiveness of the developed high-throughput screening instrument for optimizing photocatalytic water purification.

How machine learning can accelerate electrocatalysis discovery and optimization

Advances in machine learning (ML) provide the means to bypass bottlenecks in the discovery of new electrocatalysts using traditional approaches. In this review, we highlight the currently achieved work in ML-accelerated discovery and optimization of electrocatalysts via a tight collaboration between computational models and experiments. First, the applicability of available methods for constructing machine-learned potentials (MLPs), which provide accurate energies and forces for atomistic simulations, are discussed. Meanwhile, the current challenges for MLPs in the context of electrocatalysis are highlighted. Then, we review the recent progress in predicting catalytic activities using surrogate models, including microkinetic simulations and more global proxies thereof. Several typical applications of using ML to rationalize thermodynamic proxies and predict the adsorption and activation energies are also discussed. Next, recent developments of ML-assisted experiments for catalyst characterization, synthesis optimization and reaction condition optimization are illustrated. In particular, the applications in ML-enhanced spectra analysis and the use of ML to interpret experimental kinetic data are highlighted. Additionally, we also show how robotics are applied to high-throughput synthesis, characterization and testing of electrocatalysts to accelerate the materials exploration process and how this equipment can be assembled into self-driven laboratories.

Artificial intelligence to bring nanomedicine to life

The technology of drug delivery systems (DDSs) has demonstrated an outstanding performance and effectiveness in production of pharmaceuticals, as it is proved by many FDA-approved nanomedicines that have an enhanced selectivity, manageable drug release kinetics and synergistic therapeutic actions. Nonetheless, to date, the rational design and high-throughput development of nanomaterial-based DDSs for specific purposes is far from a routine practice and is still in its infancy, mainly due to the limitations in scientists' capabilities to effectively acquire, analyze, manage, and comprehend complex and ever-growing sets of experimental data, which is vital to develop DDSs with a set of desired functionalities. At the same time, this task is feasible for the data-driven approaches, high throughput experimentation techniques, process automatization, artificial intelligence (AI) technology, and machine learning (ML) approaches, which is referred to as The Fourth Paradigm of scientific research. Therefore, an integration of these approaches with nanomedicine and nanotechnology can potentially accelerate the rational design and high-throughput development of highly efficient nanoformulated drugs and smart materials with pre-defined functionalities. In this Review, we survey the important results and milestones achieved to date in the application of data science, high throughput, as well as automatization approaches, combined with AI and ML to design and optimize DDSs and related nanomaterials. This manuscript mission is not only to reflect the state-of-art in data-driven nanomedicine, but also show how recent findings in the related fields can transform the nanomedicine's image. We discuss how all these results can be used to boost nanomedicine translation to the clinic, as well as highlight the future directions for the development, data-driven, high throughput experimentation-, and AI-assisted design, as well as the production of nanoformulated drugs and smart materials with pre-defined properties and behavior. This Review will be of high interest to the chemists involved in materials science, nanotechnology, and DDSs development for biomedical applications, although the general nature of the presented approaches enables knowledge translation to many other fields of science.

Use of metamodels for rapid discovery of narrow bandgap oxide photocatalysts

New photocatalysts are traditionally identified through trial-and-error methods. Machine learning has shown considerable promise for improving the efficiency of photocatalyst discovery from a large potential pool. Here, we describe a multi-step, target-driven consensus method using a stacking meta-learning algorithm that robustly predicts bandgaps and H2 evolution activities of photocatalysts. Trained on small datasets, these models can rapidly screen a large space (>10 million materials) to identify promising, non-toxic compounds as candidate water splitting photocatalysts. Two effective compounds and two controls possessing optimal bandgap values (∼2 eV) but not photoactivity as predicted by the models were synthesized. Their experimentally measured bandgaps and H2 evolution activities were consistent with the predictions. Conspicuously, the two compounds with strong photoactivities under UV and visible light are promising visible-light-driven water splitting photocatalysts. This study demonstrates the power of machine learning and the potential of big data to accelerate discovery of next-generation photocatalysts.

Synchrotron infrared spectroscopic high-throughput screening of multi-composite photocatalyst films for air purification

Synchrotron-based infrared microscope was used for the high-throughput screening of Fe³⁺/Nb⁵⁺ doped TiO₂ photocatalysts for air purification.

A Laser-Activated Membrane Introduction Mass Spectrometry Study of Proton Spillover Promoted Alkane Dehydrogenation

Operando high-throughput evaluation of heterogeneous catalysts by laser-activated membrane introduction mass spectrometry (LAMIMS) elucidates the Pt loading dependence of methylcyclohexane dehydrogenation on platinized γ-alumina beads. A CO₂ marking laser rapidly and sequentially heats catalyst beads positioned on a heat-dissipating carbon paper support that overlays a silicone membrane, separating the bead library reaction zone from a quadrupole mass analyzer. The toluene m/z peak varies logarithmically with Pt loading, suggesting that reactivity includes factors that are negatively correlated to Pt loading. These factors may include the Pt/γ-Al₂O₃ surface interfacial region as one component of a heterogeneous catalytically active surface area/mass. This work demonstrates LAMIMS as a broadly applicable high-throughput operando screening method for heterogeneous catalysts.

Surface-bound sacrificial electron donors in promoting photocatalytic reduction on titania nanocrystals

Titania nanocrystals have been investigated for fast color switching through photocatalytic reduction of dyes and hexacyanometalate pigments. Here we reveal that direct binding of sacrificial electron donors (SEDs) to the surface of titania nanocrystals can significantly promote the charge transfer rate by more efficiently scavenging photogenerated holes and releasing more photogenerated electrons for reduction reactions. Using diethylene glycol (DEG) as an example, we show that its binding to the nanoparticle surface, which can be achieved either during or after the nanoparticle formation, greatly enhances the photocatalytic reduction in comparison with the case where free DEG molecules are simply added as external SEDs.

Thin-Film Processing Routes for Combinatorial Materials Investigations-A Review

High-throughput combinatorial investigations are transforming materials discovery, phase diagram development, and processing optimization. Thin-film deposition techniques are frequently used to fabricate sample libraries employed in these studies. Various adaptations of well-known thin-film chemical vapor deposition (CVD) and physical vapor deposition (PVD) techniques utilized for the synthesis of inorganic combinatorial thin-film materials libraries are reviewed, with novel processing approaches being highlighted. Methods for developing gradients in composition of other film properties are described. Issues and considerations specific to thin-film processing of combinatorial materials libraries are discussed, with some emphasis on catalytic applications.

High-throughput analysis of photocatalytic reactivity of differing TiO2 formulations using 96-well microplate reactors

The rapid development of photocatalysts for water decontamination benefits from availability of sensitive platforms for screening photocatalytic reactivity. The standard approach typically involves quantifying the degradation of a single dye compound in a slurry system in individual beakers, which requires tedious photocatalyst separation and long operation time. We present a simple and efficient method for assessing the photocatalytic activity of different photocatalyst nanomaterials that eliminates the solid separation process. The 96-well microplate method demonstrated an improved applicability as a high-throughput screening method for photocatalytic reaction mechanisms using a wide range of chemical substrates (i.e., methyl orange, methylene blue, terephthalic acid, and β-nicotinamide adenine dinucleotide coenzyme) and photocatalyst concentrations (1-100 mg/L). By employing photocatalysts at lower concentrations compared to the slurry system, rapid screening was accomplished through direct spectrophotometric or spectrofluorometric measurements. The mass-normalized rate constants of dye degradation were used to determine the photocatalytic reactivity of three commercial TiO₂ nanomaterials, which followed an order of SRM TiO₂ 1898 ≈ Degussa TiO₂ P90 > Food-grade TiO₂ E171. The extent of hydroxyl radical involvement in methyl orange degradation was estimated to be ∼74% by using radical scavengers in the microplate reactor. Given the utilization of low-concentration photocatalyst, this protocol may be used for evaluating photocatalytic reactivity and oxidative stress caused by photocatalyst exposure in an aquatic environment. We further evaluated photocatalytic reaction kinetics with respect to energetic and photonic efficiency. The method could greatly facilitate comparisons across different laboratories when quantifying photocatalytic reactivity and efficiency, which would aid in standardizing bench-scale photocatalysis testing.

TiO2 Photocatalysis in Aromatic "Redox Tag"-Guided Intermolecular Formal [2 + 2] Cycloadditions

Since the pioneering work by Macmillan, Yoon, and Stephenson, homogeneous photoredox catalysis has occupied a central place in new reaction development in the field of organic chemistry. While heterogeneous semiconductor photocatalysis has also been studied extensively, it has generally been recognized as a redox option in inorganic chemistry where such "photocatalysis" is most often used to catalyze carbon-carbon bond cleavage and not in organic chemistry where bond formation is usually the focal point. Herein, we demonstrate that titanium dioxide photocatalysis is a powerful redox option to construct carbon-carbon bonds by using intermolecular formal [2 + 2] cycloadditions as models. Synergy between excited electrons and holes generated upon irradiation is expected to promote the overall net redox neutral process. Key for the successful application is the use of a lithium perchlorate/nitromethane electrolyte solution, which exhibits remarkable Lewis acidity to facilitate the reactions of carbon-centered radical cations with carbon nucleophiles. The reaction mechanism is reasonably understood based on both intermolecular and intramolecular single electron transfer regulated by an aromatic "redox tag". Most of the reactions were completed in less than 30 min even in aqueous and/or aerobic conditions without the need for sacrificial reducing or oxidizing substrates generally required for homogeneous photoredox catalysis.

Compositionally Dependent Nonlinear Optical Bandgap Behavior of Mixed Anodic Oxides in Niobium-Titanium System

Optical bandgap mapping of Nb-Ti mixed oxides anodically grown on a thin film parent metallic combinatorial library was performed via variable angle spectroscopic ellipsometry (VASE). A wide Nb-Ti compositional spread ranging from Nb-90 at.% Ti to Nb-15 at.% Ti deposited by cosputtering was used for this purpose. The Nb-Ti library was stepwise anodized at potentials up to 10 V SHE, and the anodic oxides optical properties were mapped along the Nb-Ti library with 2 at.% resolution. The surface dissimilarities along the Nb-Ti compositional gradient were minimized by tuning the deposition parameters, thus allowing a description of the mixed Nb-Ti oxides based on a single Tauc-Lorentz oscillator for data fitting. Mapping of the Nb-Ti oxides optical bandgap along the entire compositional spread showed a clear deviation from the linear model based on mixing individual Nb and Ti electronegativities proportional to their atomic fractions. This is attributed to the strong amorphization and an in-depth compositional gradient of the mixed oxides. A systematic optical bandgap decrease toward values as low as 2.0 eV was identified at approximately 50 at.% Nb. Mixing of Nb₂O₅ and TiO₂ with both amorphous and crystalline phases is concluded, whereas the possibility of complex Nb_aTi_bO_y oxide formation during anodization is unlikely.

Combinatorial fabrication of composite nanorods using oblique angle co-deposition

We demonstrate that oblique angle co-deposition can be used as a versatile combinatory nanofabrication technique to generate a library of nanomaterials. Using the Cu-Fe2O3 system as an example, by carefully characterizing the vapor plumes of the source materials, a composition map can be generated, which is used to design the locations of all the substrate holders. The resulting nanostructures at different locations show different thickness, morphology, crystallinity, composition, as well as inhomogeneity in microstructures, and material maps of all these structural parameters are established. By further oxidizing or reducing the composite nanostructures, their properties-such as band gap, photocatalytic performance, and magnetic properties-can be easily linked to their composition and other structural parameters. Optimal materials for photocatalytic and magnetic applications are efficiently identified. It is expected that oblique angle co-deposition and its variations could become the most powerful combinatory nanofabrication technique for nanomaterial survey.