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Polycrystalline alloy electrodes of Pt with alkaline earth metals (Ca, Sr, and Ba) have been shown to exhibit enhanced electrocatalytic performance for oxygen reduction reaction (ORR) relative to Pt electrodes. The large oxophilicity of the alkaline earth metals makes it challenging to synthesize such alloys. Here, we synthesize a carbon-supported platinum−magnesium (PtMg) alloy with enhanced catalytic activity and durability for the ORR in both a half-cell and single cell when compared to the state-of-the-art Pt/C catalyst. Employing metallic Mg powder as a precursor can overcome the large oxophilicity of Mg and induce alloying of Mg with Pt, whereas conventional Mg salts do not form an alloy. Density functional theory calculations elucidate the origin of the enhanced catalytic activity and durability. Complementary physical and electrochemical analyses also evidence them in this work. This material holds great application potential and will contribute to elucidation of the effects of alloying Pt with electropositive metals.

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Dental caries are one of the chronic diseases caused by organic acids made from oral microbes. However, there was a lack of knowledge about the oral microbiome of Korean children. The aim of this study was to analyze the metagenome data of the oral microbiome obtained from Korean children and to discover bacteria highly related to dental caries with machine learning models. Saliva and plaque samples from 120 Korean children aged below 12 years were collected. Bacterial composition was identified using Illumina HiSeq sequencing based on the V3–V4 hypervariable region of the 16S rRNA gene. Ten major genera accounted for approximately 70% of the samples on average, including Streptococcus, Neisseria, Corynebacterium, and Fusobacterium. Differential abundant analyses revealed that Scardovia wiggsiae and Leptotrichia wadei were enriched in the caries samples, while Neisseria oralis was abundant in the non-caries samples of children aged below 6 years. The caries and non-caries samples of children aged 6–12 years were enriched in Streptococcus mutans and Corynebacterium durum, respectively. The machine learning models based on these differentially enriched taxa showed accuracies of up to 83%. These results confirmed significant alterations in the oral microbiome according to dental caries and age, and these differences can be used as diagnostic biomarkers.

Oxygen-based electrocatalysis is an integral aspect of a clean and sustainable energy conversion/storage system. The development of economic bifunctional electrocatalysts with high activity and durability during reversible reactions remains a great challenge. The tailored porous structure and separately presented active sites for oxygen reduction and oxygen evolution reactions (ORR and OER) without mutual interference are most crucial for achieving desired bifunctional catalysts. Here, we report a hybrid composed of sheath–core cobalt oxynitride (CoO x @CoN y ) nanorods grown perpendicularly on N-doped carbon nanofiber (NCNF). The brush-like CoO x @CoN y nanorods, composed of metallic Co4N cores and oxidized surfaces, exhibit excellent OER activity (E = 1.69 V at 10 mA cm–2) in an alkaline medium. Although pristine NCNF or CoO x @CoN y alone had poor catalytic activity in the ORR, the hybrid showed dramatically enhanced ORR performance (E = 0.78 V at −3 mA cm–2). The experimental results coupled with a density functional theory (DFT) simulation confirmed that the broad surface area of the CoO x @CoN y nanorods with an oxidized skin layer boosts the catalytic OER, while the facile adsorption of ORR intermediates and a rapid interfacial charge transfer occur at the interface between the CoO x @CoN y nanorods and the electrically conductive NCNF. Furthermore, it was found that the independent catalytic active sites in the CoO x @CoN y /NCNF catalyst are continuously regenerated and sustained without mutual interference during the round-trip ORR/OER, affording stable operation of Zn–air batteries.

Periodontitis is a widespread chronic inflammatory disease caused by interactions between periodontal bacteria and homeostasis in the host. We aimed to investigate the performance and reliability of machine learning models in predicting the severity of chronic periodontitis. Mouthwash samples from 692 subjects (144 healthy controls and 548 generalized chronic periodontitis patients) were collected, the genomic DNA was isolated, and the copy numbers of nine pathogens were measured using multiplex qPCR. The nine pathogens are as follows: Porphyromonas gingivalis (Pg), Tannerella forsythia (Tf), Treponema denticola (Td), Prevotella intermedia (Pi), Fusobacterium nucleatum (Fn), Campylobacter rectus (Cr), Aggregatibacter actinomycetemcomitans (Aa), Peptostreptococcus anaerobius (Pa), and Eikenella corrodens (Ec). By adding the species one by one in order of high accuracy to find the optimal combination of input features, we developed an algorithm that predicts the severity of periodontitis using four machine learning techniques. The accuracy was the highest when the models classified “healthy” and “moderate or severe” periodontitis (H vs. M-S, average accuracy of four models: 0.93, AUC = 0.96, sensitivity of 0.96, specificity of 0.81, and diagnostic odds ratio = 112.75). One or two red complex pathogens were used in three models to distinguish slight chronic periodontitis patients from healthy controls (average accuracy of 0.78, AUC = 0.82, sensitivity of 0.71, and specificity of 0.84, diagnostic odds ratio = 12.85). Although the overall accuracy was slightly reduced, the models showed reliability in predicting the severity of chronic periodontitis from 45 newly obtained samples. Our results suggest that a well-designed combination of salivary bacteria can be used as a biomarker for classifying between a periodontally healthy group and a chronic periodontitis group.

Herein, binary heteronanosheets made of ultrathin ReS 2 nanosheets and reduced graphene oxide (RGO) with either a two-dimensional (2D) "sheet-on-sheet" architecture (2D ReS 2 /RGO) or a three-dimensional hierarchical structure (3D ReS 2 /RGO) are constructed through rational structureengineering strategies. In the resultant 3D ReS 2 /RGO heteronanosheets, the ultrathin ReS 2 nanosheets are bridged on the RGO surface through Re−O bonds in a vertically oriented manner, which endows the heteronanosheets with open frameworks and a hierarchical porous structure. In sharp contrast to the 2D ReS 2 /RGO, the 3D ReS 2 /RGO heteronanosheets are featured with abundant active sites and channels for efficient electrolyte ions transport. This, coupled with the strong affinity toward oxygen-containing intermediates intrinsically associated with the binary ReS 2 /RGO structure, imparts excellent oxygen reduction performance to the 3D ReS 2 /RGO heteronanosheets for potential applications in fuel cells and metal−air batteries.

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