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Abstract The Open Cluster Chemical Abundances and Mapping (OCCAM) survey seeks to curate a large, comprehensive, uniform dataset of open clusters and member stars to constrain key Galactic parameters. This eighth entry from the OCCAM survey, based on the newly released Sloan Digital Sky Survey V/Milky Way Mapper Data Release 19 (DR19), has established a sample of 158 quality open clusters that are used to constrain the radial and azimuthal gradients of the Milky Way. The DR19 cluster sample [Fe/H] abundances are largely consistent with measurements from other large-scale spectroscopic surveys. However, the gradients we calculate deviate considerably for some elements. We find an overall linear Galactic radial [Fe/H] gradient of −0.079 ± 0.006 dex kpc⁻¹using the cluster’s current Galactocentric radius and a gradient of −0.071 ± 0.005 dex kpc⁻¹with respect to the cluster’s guiding center radius. We do not find strong evidence for significant evolution of the differential element gradients ([X/Fe]) investigated here (O, Mg, Si, S, Ca, Ti, Cr, Mn, Co, Ni, Na, Al, K, Ce, Nd), and instead show indications that the radial [Fe/H] gradient does not change significantly with stellar population age. For the first time, using the OCCAM sample, we have sufficient numbers of clusters to investigate Galactic azimuthal variations. In this work, we find evidence of azimuthal variations in the measured radial abundance gradient in the Galactic disk using our open cluster sample. 

Abstract In the coming years, detailed chemical abundances from large-scale high-resolution spectroscopic surveys will become available for vast numbers of stars across the Milky Way. Previous work has suggested that abundance ratios from these spectra can allow us to estimate ages from a large number of stars. These data will be leveraged to calibrate chemical clocks to age-date field stars, as reliable stellar ages remain elusive. In this work, we extended our empirical relationship between stellar age and their carbon-to-nitrogen ([C/N]) abundance ratio for evolved stars to older and more metal-poor stars by combining the original open cluster calibration sample and four globular clusters: 47 Tuc, M 71, M 4, and M 5. With this extension, [C/N] can be used as a chemical clock for evolved field stars to investigate not only regions within the metal-rich disk but also more metal-poor regions of our Galaxy. We have established the [C/N]–age relationship for Apache Point Observatory Galactic Evolution Experiment (APOGEE) DR17 red giant stars, which have experienced the first dredge-up but have not yet undergone any extramixing, in clusters usable for ages between $8.62\le \log \left(\mathrm{Age}\right[\mathrm{yr}\left]\right)\le 10.13$and for metallicities of −1.2 ≤ [Fe/H] ≤ +0.3. This relationship can be uniformly applied to these stars within the APOGEE DR17 sample. This measured [C/N]–age APOGEE DR17 relationship is also shown to be consistent with stellar ages derived from asteroseismic results of APOKASC and APO-K2. 

Early identification and survey of biological analytes, including neurotransmitters and hormones, are essential for early disease diagnosis, tailored treatment, and immediate physiological evaluation. Dysregulations in dopamine, cortisol, oxytocin, and serotonin are associated with a wide array of illnesses, encompassing neurodegenerative, psychiatric, cardiovascular, and metabolic conditions. In this study, we present an AI-assisted sensing platform utilizing reduced graphene quantum dots (RGQDs) for the multiplex detection of analytes (dopamine, serotonin, cortisol, and oxytocin) by spectral fingerprint identification generated due to analyte-binding-facilitated fluorescence quenching. RGQDs exhibit photostable near-infrared emission, allowing for signal detection through the layers of biological tissue and remarkable in vivo biocompatibility, altogether enabling their utility in implanted or wearable diagnostic systems. In contrast to conventional sensors that depend on molecular specificity, this study employs cross- reactive RGQDs, whose fluorescence is influenced by noncovalent interactions with aforementioned analytes, evaluated via the conformer−rotamer sampling tool (CREST). This theoretical model of interactions of analyte with the RGQD surface computes optimized dimer geometries, binding energies, and π−π stacking configurations, revealing analyte-specific noncovalent binding patterns that enable selective sensing. Artificial intelligence models are developed and trained to recognize spectral variations unique to each analyte. Through unsupervised dimensionality reduction and nonlinear classification (UMAP combined with a random forest model), the system distinguishes analytes using three key spectral features: continuum-removed emission area, first-derivative amplitudes, and wavelet detail energy at level 4, achieving an overall discrimination accuracy of 91.2% and a macro F1 score of 91.5%. This approach facilitates multiplexed nonspecific sensing of four critical hormones/neurotransmitters without the need for designing complex analyte-specific surface groups. This work beneficially merges material innovation with machine learning to provide a cost-efficient, noninvasive, and scalable diagnostic platform, aimed to enhance healthcare accessibility in both clinical and underserved environments. 

The growing threat of antibiotic-resistant bacteria necessitates the development of alternative antimicrobial agents. Gallium oxyhydroxide (GaOOH) is a promising candidate, though its direct antibacterial efficacy is unexplored. This study provides the first direct evidence of GaOOH microparticles exhibiting cytotoxic effects against both Gram-positive Staphylococcus aureus (S.aureus) and Gram-negative Escherichia coli (E. coli). Orthorhombic GaOOH particles were synthesized hydrothermally, with their morphology influenced by the pH of the synthesis process, as confirmed by scanning electron microscopy and x-ray diffraction analysis. Antibacterial assays revealed that cytotoxicity against E. coli increases with a higher synthesis pH, a trend we demonstrate to be associated with the enhanced defect density in particles, as supported by photoluminescence spectra and FTIR analysis. The study underscores the significant influence of synthesis conditions on the morphology and crystallinity of the resulting GaOOH microparticles, highlighting the influence of surface characteristics on antibacterial agents.