Effect of Passivation on Stability and Electronic Structure of Bulk-like ZnO Clusters

Electronic structure of nearly stoichiometric and nonstoichiometric clusters of ZnO having bulk-like wurtzite geometry passivated with fictitious hydrogen atoms are comparatively analyzed for structural evolution using density functional theory-based electronic structure calculations. A parameter, average binding energy per atomic number (ABE-number), is introduced for better insight of structural evolution. The stability of a cluster is determined by binding energy per atom and ABE-number, whereas structural evolution on the basis of spin-polarized energy spectrum is studied via site projected partial density of states (l-DOS). The overall structural evolution is mapped for bare and passivated ZnO clusters to l-DOS. The study has established a correlation between the stability of clusters and their l-DOS. O-excess and O-surfaced clusters are found to be more stable. The HOMO–LUMO gap varies from 0 to 6.3 eV by tuning the size, composition, and surface termination of the clusters. Present results reported for clusters of sizes up to ∼1 nm can pave a path for formulating strategies for experimental synthesis of ZnO nanoparticles for tuning the HOMO–LUMO gap

EPR Evidence of Liquid Water in Ice: An Intrinsic Property of Water or a Self-Confinement Effect?

Liquid water (LW) existence in pure ice below 273 K has been a controversial aspect primarily because of the lack of experimental evidence. Recently, electron paramagnetic resonance (EPR) has been used to study deeply supercooled water in a rapidly frozen polycrystalline ice. The same technique can also be used to probe the presence of LW in polycrystalline ice that has formed through a more conventional, slow cooling one. In this context, the present study aims to emphasize that in case of an external probe involving techniques such as EPR, the results are influenced by the binary phase (BP) diagram of the probe-water system, which also predicts the existence of LW domains in ice, up to the eutectic point. Here we report the results of our such EPR spin-probe studies on water, which demonstrate that smaller the concentration of the probe stronger is the EPR evidence of liquid domains in polycrystalline ice. We used computer simulations based on stochastic Liouville theory to analyze the lineshapes of the EPR spectra. We show that the presence of the spin probe modifies the BP diagram of water, at very low concentrations of the spin probe. The spin probe thus acts, not like a passive reporter of the behavior of the solvent and its environment, but as an active impurity to influence the solvent. We show that there exists a lower critical concentration, below which BP diagram needs to be modified, by incorporating the effect of confinement of the spin probe. With this approach, we demonstrate that the observed EPR evidence of LW domains in ice can be accounted for by the modified BP diagram of the probe–water system. The present work highlights the importance of taking cognizance of the possibility of spin probes affecting the host systems, when interpreting the EPR (or any other probe based spectroscopic) results of phase transitions of host, as its ignorance may lead to serious misinterpretations

Toward Developing Techniques─Agnostic Machine Learning Classification Models for Forensically Relevant Glass Fragments

Glass fragments found in crime scenes may constitute important forensic evidence when properly analyzed, for example, to determine their origin. This analysis could be greatly helped by having a large and diverse database of glass fragments and by using it for constructing reliable machine learning (ML)-based glass classification models. Ideally, the samples that make up this database should be analyzed by a single accurate and standardized analytical technique. However, due to differences in equipment across laboratories, this is not feasible. With this in mind, in this work, we investigated if and how measurement performed at different laboratories on the same set of glass fragments could be combined in the context of ML. First, we demonstrated that elemental analysis methods such as particle-induced X-ray emission (PIXE), laser ablation induct i v e l y coupled plasma mass spectrometry (LA-ICP-MS), scanning electron microscopy with energy-dispersive X-ray spectrometry (SEM-EDS), particle-induced Gamma-ray emission (PIGE), instrumental neutron activation analysis (INAA), and prompt Gamma-ray neutron activation analysis (PGAA) could each produce lab-specific ML-based classification models. Next, we determined rules for the successf u l combinations of data from different laboratories and techniques and demonstrated that when followed, they give rise to improved models, and conversely, poor combinations wi l l lead to poor-performing models. Thus, the combination of PIXE and LA-ICP-MS improves the performances by similar to 10-15%, while combining PGAA with other techniques provides poorer performances in comparison with the lab-specific models. Finally, we demonstrated that the poor performances of the SEM-EDS technique, sti l l in use by law enforcement agencies, could be greatly improved by replacing SEM-EDS measurements for Fe and Ca by PIX E measurements for these elements. These findings suggest a process whereby forensic laboratories using different elemental analysis techniques could upload their data into a unified database and get reliable classification based on lab-agnostic models. This in tur n brings us closer to a more exhaustive extraction of information from glass fragment evidence and furthermore may form the basis for international-wide collaboration between law enforcement agencies.Peer reviewe

Assessing the burden of COVID-19 among children aged 6-14 years in Karnataka, India: A cross-sectional survey

Background: India experienced three coronavirus disease (COVID-19) waves, with the third attributed to the highly contagious Omicron variant. Before the national vaccination rollout for children above 6, understanding severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) immunoglobulin G (IgG) positivity in the pediatric population was essential. This study aims to assess the burden of Covid-19 infection and to estimate the seroprevalence in children aged 6 to 14 years in the state of Karnataka. Material and Methods: We surveyed 5,358 children aged 6-14 across Karnataka using 232 health facilities, from June 6 to 14, 2022. We determined the sample size using the PPS (Population Proportional to Size) technique and employed cluster sampling. We tested all participants for SARS-CoV-2 IgG with an enzyme-linked immunosorbent assay (ELISA) kit and SARS-CoV-2 RNA with reverse transcription-polymerase chain reaction (RT-PCR). We sequenced samples with a cycle threshold (CT) value below 25 using whole genomic sequencing (WGS). Result: We found an adjusted seroprevalence of IgG at 75.38% statewide, and we found 0.04% of children RT-PCR positive for COVID-19. We determined a case-to-infection ratio of 1:37 and identified the SARS-CoV-2 strains as Omicron, BA.5, and BA.2.10. Conclusion: The study showed a high seroprevalence of IgG among children with low active infection. Omicron, BA. 5, and BA. 2.10 variants were detected through WGS