We believe that this approach can pave the way for designing noble metal-based carbon nanocomposites for a variety of applications, ranging from environmental redemption to electrochemical energy harvesting. As case studies, we have explored the nanocomposites for various catalytic activities and found them to be very competent with recently reported various state-of-the-art electrocatalysts and their commercial counterparts.A discovery-based lipid profiling study of serum samples from a cohort that included patients with clear cell renal cell carcinoma (ccRCC) stages I, II, III, and IV (n = 112) and controls (n = 52) was performed using ultraperformance liquid chromatography coupled to quadrupole-time-of-flight mass spectrometry and machine learning techniques. Multivariate models based on support vector machines and the LASSO variable selection method yielded two discriminant lipid panels for ccRCC detection and early diagnosis. A 16-lipid panel allowed discriminating ccRCC patients from controls with 95.7% accuracy in a training set under cross-validation and 77.1% accuracy in an independent test set. A second model trained to discriminate early (I and II) from late (III and IV) stage ccRCC yielded a panel of 26 compounds that classified stage I patients from an independent test set with 82.1% accuracy. Thirteen species, including cholic acid, undecylenic acid, lauric acid, LPC(160/00), and PC(182/182), identified with level 1 exhibited significantly lower levels in samples from ccRCC patients compared to controls. Moreover, 3α-hydroxy-5α-androstan-17-one 3-sulfate, cis-5-dodecenoic acid, arachidonic acid, cis-13-docosenoic acid, PI(160/181), PC(160/182), and PC(O-160/204) contributed to discriminate early from late ccRCC stage patients. The results are auspicious for early ccRCC diagnosis after validation of the panels in larger and different cohorts.Facet-engineered monoclinic scheelite BiVO4 particles decorated with various cocatalysts were successfully synthesized by selective sunlight photodeposition of metal or metal oxy(hydroxide) nanoparticles onto the facets of truncated bipyramidal BiVO4 monoclinic crystals coexposing 010 and 110 facets. X-ray photoelectron spectroscopy, scanning electron microscopy, and scanning Auger microscopy revealed that metallic silver (Ag) and cobalt (oxy)hydroxide (CoO x (OH) y ) particles were selectively deposited onto the 010 and 110 facets, respectively, regardless of the cocatalyst amount. By contrast, the nickel (oxy)hydroxide (NiO x (OH) y ) photodeposition depends on the nickel precursor amount with an unprecedented selectivity for 0.1 wt % NiO x (OH) y /BiVO4 with a preferential deposition onto the 010 facets and the edges between the 110 facets. Moreover, these noble metal-free heterostructures led to remarkable photocatalytic properties for rhodamine B photodecomposition and sacrificial water oxidation reactions. For instance, 0.2 wt % CoO x (OH) y /BiVO4 led to one of the highest oxygen evolution rates, i.e., 1538 μmol h-1 g-1, ever described which is ten times higher than that found for bare BiVO4. The selective deposition of cobalt (oxy)hydroxide species onto the more electron-deficient facet of truncated bipyramidal monoclinic BiVO4 particles favors photogenerated charge carrier separation and therefore plays a key role for efficient photochemical oxygen evolution.Currently, the most powerful approach to monitor organic micropollutants (OMPs) in environmental samples is the combination of target, suspect, and nontarget screening strategies using high-resolution mass spectrometry (HRMS). However, the high complexity of sample matrices and the huge number of OMPs potentially present in samples at low concentrations pose an analytical challenge. Ion mobility separation (IMS) combined with HRMS instruments (IMS-HRMS) introduces an additional analytical dimension, providing extra information, which facilitates the identification of OMPs. The collision cross-section (CCS) value provided by IMS is unaffected by the matrix or chromatographic separation. Consequently, the creation of CCS databases and the inclusion of ion mobility within identification criteria are of high interest for an enhanced and robust screening strategy. In this work, a CCS library for IMS-HRMS, which is online and freely available, was developed for 556 OMPs in both positive and negative ionization modes using electrospray ionization. The inclusion of ion mobility data in widely adopted confidence levels for identification in environmental reporting is discussed.  https://www.selleckchem.com/products/diphenhydramine.html  Illustrative examples of OMPs found in environmental samples are presented to highlight the potential of IMS-HRMS and to demonstrate the additional value of CCS data in various screening strategies.Aromatase, or cytochrome P450 19A1, catalyzes the aromatization of androgens to estrogens within the body. Changes in the activity of this enzyme can produce hormonal imbalances that can be detrimental to sexual and skeletal development. Inhibition of this enzyme can occur with drugs and natural products as well as environmental chemicals. Therefore, predicting potential endocrine disruption via exogenous chemicals requires that aromatase inhibition be considered in addition to androgen and estrogen pathway interference. Bayesian machine learning methods can be used for prospective prediction from the molecular structure without the need for experimental data. Herein, the generation and evaluation of multiple machine learning models utilizing different sources of aromatase inhibition data are described. These models are applied to two test sets for external validation with molecules relevant to drug discovery from the public domain. In addition, the performance of multiple machine learning algorithms was evaluated by comparing internal five-fold cross-validation statistics of the training data. These methods to predict aromatase inhibition from molecular structure, when used in concert with estrogen and androgen machine learning models, allow for a more holistic assessment of endocrine-disrupting potential of chemicals with limited empirical data and enable the reduction of the use of hazardous substances.