Controlled delivery systems play a critical role in the success of bone morphogenetic proteins (i.e., BMP2 and BMP7) for challenged bone repair. Instead of single-drug release that is currently and commonly prevalent, dual-drug delivery strategies are highly desired to achieve effective bone regeneration because natural bone repair process is driven by multiple factors. Particularly, angiogenesis is essential for osteogenesis and requires more than just one factor (e.g., Vascular Endothelial Growth Factor, VEGF). Therefore, we developed a novel mesoporous silicate nanoparticles (MSNs) incorporated-3D nanofibrous gelatin (GF) scaffold for dual-delivery of BMP2 and deferoxamine (DFO). DFO is a hypoxia-mimetic drug that can activate hypoxia-inducible factor-1 alpha (HIF-1α), and trigger subsequent angiogenesis. Sustained BMP2 release system was achieved through encapsulation into large-pored MSNs, while the relative short-term release of DFO was engineered through covalent conjugation with chitosan to reduce its cytotoxicity and elongate its half-life. Both MSNs and DFO were incorporated onto a porous 3D GF scaffold to serve as a biomimetic osteogenic microenvironment. Our data indicated that DFO and BMP2 were released from a scaffold at different release rates (10 vs 28 days) yet maintained their angiogenic and osteogenic ability, respectively. Importantly, our data indicated that the released DFO significantly improved BMP2-induced osteogenic differentiation where the dose/duration was important for its effects in both mouse and human stem cell models. Thus, we developed a novel and tunable MSNs/GF 3D scaffold-mediated dual-drug delivery system and studied the potential application of the both FDA-approved DFO and BMP2 for bone tissue engineering.

Herein, we have reported hierarchical mesoporous RuO2/Cu2O nanoparticle-catalyzed selective aerobic oxidative homo/hetero azo-coupling of anilines under oxidant/base/additive-free conditions. The synergistic effect of the individual oxides was established by the density functional theory calculations and experimental studies.

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Earth–abundant transition metal chalcogenide materials are of great research interest for energy production and environmental remediation, as they exhibit better photocatalytic activity due to their suitable electronic and optical properties. This study focuses on the photocatalytic activity of flower-like SnS2 nanoparticles (composed of nanosheet subunits) embedded in TiO2 synthesized by a facile hydrothermal method. The materials were characterized using different techniques, and their photocatalytic activity was assessed for hydrogen evolution reaction and the degradation of methylene blue. Among the catalysts studied, 10 wt. % of SnS2 loaded TiO2 nanocomposite shows an optimum hydrogen evolution rate of 195.55 µmolg−1, whereas 15 wt. % loading of SnS2 on TiO2 exhibits better performance against the degradation of methylene blue (MB) with the rate constant of 4.415 × 10−4 s−1 under solar simulated irradiation. The improved performance of these materials can be attributed to the effective photo-induced charge transfer and reduced recombination, which make these nanocomposite materials promising candidates for the development of high-performance next-generation photocatalyst materials. Further, scavenging experiments were carried out to confirm the reactive oxygen species (ROS) involved in the photocatalytic degradation. It can be observed that there was a 78% reduction in the rate of degradation when IPA was used as the scavenger, whereas around 95% reduction was attained while N2 was used as the scavenger. Notably, very low degradation (<5%) was attained when the dye alone was directly under solar irradiation. These results further validate that the •OH radical and the superoxide radicals can be acknowledged for the degradation mechanism of MB, and the enhancement of degradation efficiency may be due to the combined effect of in situ dye sensitization during the catalysis and the impregnation of low bandgap materials on TiO2.

Regioselective synthesis of 2-alkoxyimidazopyridines by Fe-SBA-15 and screening of their in silico binding affinity to biological targets.

The electrochemical oxidation mechanism of alkali lignin in the presence of a laccase–mediator system (LMS) in an aerobic environment.

Machine learning algorithms have been applied successfully in many areas of materials chemistry but often suffer from an inability to extract chemical insight. To demonstrate that an approach combining machine learning and chemical intuition can be effective in generating interpretable models, the structures of binary equiatomic rare-earth intermetallics RX, whose relationships have long defied understanding, were investigated as a case study. A structure map was developed based on only two parameters, which are derived from simple elemental descriptors (atomic number, period and group numbers, radii, and electronegativity) of the R and X components. This map reveals the previously hidden patterns of structural regularities of RX intermetallics. It explains the preference for CsCl-, TlI-, or FeB-type structures among these compounds, predicts the structures of missing members, rationalizes the occurrence of metastable phases and polymorphic transformations, and offers strategies for structure stabilization through the addition of a third component.

Machine-learning methods have exciting potential to aid materials discovery, but their wider adoption can be hindered by the opaqueness of many models. Even if these models are accurate, the inability to understand the basis for the predictions breeds skepticism. Thus, it is imperative to develop machine-learning models that are explainable and interpretable so that researchers can judge for themselves if the predictions are consistent with their own scientific understanding and chemical insight. In this spirit, the sure independence screening and sparsifying operator (SISSO) method was recently proposed as an effective way to identify the simplest combination of chemical descriptors needed to solve classification and regression problems in materials science. This approach uses domain overlap (DO) as the criterion to find the most informative descriptors in classification problems, but sometimes a low score can be assigned to useful descriptors when there are outliers or when samples belonging to a class are clustered in different regions of the feature space. Here, we present a hypothesis that the performance can be improved by implementing decision trees (DT) instead of DO as the scoring function to find the best descriptors. This modified approach was tested on three important structural classification problems in solid-state chemistry: perovskites, spinels, and rare-earth intermetallics. In all cases, the DT scoring gave better features and significantly improved accuracies of ≥0.91 for the training sets and ≥0.86 for the test sets.