Structure of small yttrium monoxide clusters, chemical bonding, and photoionization: threshold photoionization and density functional theory investigations.

The photoionization (PI) spectra of small gas-phase yttrium monoxide clusters, YnO (n = 1-8), are investigated, and the adiabatic ionization energies are determined. The stable structures are obtained from density functional theory (DFT) calculations. The ground state structures are further confirmed by the CCSD(T) method. The PI spectra are calculated for these stable structures and are compared with the experimental PI spectra. The ground-state structures of the neutral and cation clusters are experimentally assigned with confidence on the basis of a favourable agreement between the experimental and calculated PI spectra. New structures are proposed for Y2O, Y6O, and Y8O compared to the previous literature. Y2O is a linear molecule in the ground state that was previously proposed as a C2v bent molecule. The YnO clusters become 3-dimensional from n ≥ 3. The O atom stays outside, bridging a triangular face of yttrium clusters. Chemical bonding between the yttrium and oxygen atoms is mostly ionic. The excess charge on the oxygen atom is around 1.4e-, transferred from the yttrium atoms bonded with it. Yttrium atoms are mostly covalently bonded. However, for the bigger clusters, free charges of both polarities appear on yttrium atoms that are not bonded with oxygen, indicating ionic interactions. Frontier orbitals consist of mainly delocalized 4d electrons with some 5s contributions, forming Y-Y bonding interactions, but with little contribution and zero contribution from the oxygen orbitals, regardless of the cluster size. The lost electron of YnO+ mostly comes from the 5s orbitals of all Y atoms in the cluster up to size n = 4, and then from 4d-5s hybrid orbitals from n ≥ 5, with the d contribution increasing with size. This is contrary to the previous view in the literature that photoionization occurs from a localized 4d orbital.

Emission color tuning and dual-mode luminescence thermometry design in Dy3+/Eu3+co-doped SrMoO4phosphors.

The challenge of building a highly reliable contactless temperature probe with high sensitivity, good temperature-induced color discriminability, and economical synthesis has prompted the research community to work in the field of rare-earth-based luminescence thermometry. Moreover, the fast-growing market for optoelectronic devices has increased the demand for tunable color-emitting phosphors. In this study, Dy3+/Eu3+co-doped SrMoO4phosphors were developed as tunable color-emitting source and dual-mode luminescence thermometer. A facile and cost-effective auto-combustion method was used to synthesize the phosphors. Our work demonstrates a viable scheme for tailoring the emission of single-phase phosphors by precisely controlling the dopant concentrations and by modulating excitation wavelength. The overall emission is tuned from greenish-yellow to white and greenish-yellow to reddish-orange. A detailed energy transfer process from the host to the Ln3+ions and between the Ln3+ions is discussed. Further, anti-thermal quenching in the emission of Dy3+ion is observed when excited with 297 nm. The dual-mode luminescence thermometry has been studied by analyzing the fluorescence intensity ratio of Dy3+and Eu3+ions upon excitation at 297 nm. The maximum relative sensitivity value for 4% Eu3+co-doped SrMoO4:4%Dy3+phosphor is 1.46% K-1at 300 K. Furthermore, the configurational coordinate diagram is presented to elucidate the nature of temperature-dependent emission. Therefore, our research opens up new avenues for the development of color-tunable luminescent materials for various optoelectronic and temperature-sensing applications.

Improvement in white light emission of Dy3+doped CaMoO4via Zn2+co-doping.

The research in developing a single ingredient phosphor for white-light emission is progressively increasing. It is well known that the4F9/2 â†’ 6H13/2(yellow) and4F9/2 â†’ 6H15/2(blue) transitions of Dy3+ions give near-white light emission. The white light emission of Dy3+ions can be enhanced via improving the crystallinity of the host phosphor via co-doping of transition metal ions. In this paper, we report a significant improvement in the white light emission of Dy3+doped CaMoO4by co-doping Zn2+ions. The x-ray diffraction pattern confirms the tetragonal phase of pure and doped CaMoO4phosphor. The peak broadening and a red-shift in the absorption peak are observed by UV-vis absorption analysis of Zn2+/Dy3+doped CaMoO4. From Photoluminescence studies, we have observed that in Dy3+doped CaMoO4, the 4% Dy3+doped CaMoO4exhibits maximum emission. The Zn2+ions are co-doped to further increase the luminescence intensity of CaMoO4:4%Dy3+and the maximum luminescence is obtained for 0.25% Zn2+concentration. Two intense emission peaks centered at 484 nm and 574 nm related to transitions4F9/2 â†’ 6H15/2and4F9/2 â†’ 6H13/2of Dy3+ion are observed for Dy3+doped phosphor. The4F9/2 â†’ 6H13/2transition is the forced electric dipole transition which is affected by its chemical environment. After Zn2+co-doping, the4F9/2 â†’ 6H13/2transition is affected due to a change in asymmetricity around the Dy3+ions. The 0.25% co-doping of Zn2+gives 34% enhancement in luminescence emission of 4% Dy3+doped CaMoO4. As a result, the CIE coordinates of chromaticity diagram and the color purity of the 0.25% Zn2+co-doped CaMoO4:4Dy3+show improvement in the overall white light emission. We have shown that with Zn2+co-doping, the non-radiative relaxations are reduced which results in improved white light emission of Dy3+ions.

Improved perfusion pattern score association with type 2 diabetes severity using machine learning pipeline: Pilot study.

BACKGROUND: Type 2 diabetes mellitus (T2DM) is associated with alterations in the blood-brain barrier, neuronal damage, and arterial stiffness, thus affecting cerebral metabolism and perfusion. There is a need to implement machine-learning methodologies to identify a T2DM-related perfusion pattern and possible relationship between the pattern and cognitive performance/disease severity. PURPOSE: To develop a machine-learning pipeline to investigate the method's discriminative value between T2DM patients and normal controls, the T2DM-related network pattern, and association of the pattern with cognitive performance/disease severity. STUDY TYPE: A cross-sectional study and prospective longitudinal study with a 2-year time interval. POPULATION: Seventy-three subjects (41 T2DM patients and 32 controls) aged 50-85 years old at baseline, and 42 subjects (19 T2DM and 23 controls) aged 53-88 years old at 2-year follow-up. FIELD STRENGTH/SEQUENCE: 3T pseudocontinuous arterial spin-labeling MRI. ASSESSMENT: Machine-learning-based pipeline (principal component analysis, feature selection, and logistic regression classifier) to generate the T2DM-related network pattern and the individual scores associated with the pattern. STATISTICAL TESTS: Linear regression analysis with gray matter volume and education years as covariates. RESULTS: The machine-learning-based method is superior to the widely used univariate group comparison method with increased test accuracy, test area under the curve, test positive predictive value, adjusted McFadden's R square of 4%, 12%, 7%, and 24%, respectively. The pattern-related individual scores are associated with diabetes severity variables, mobility, and cognitive performance at baseline (P < 0.05, |r| > 0.3). More important, the longitudinal change of individual pattern scores is associated with the longitudinal change of HbA1c (P = 0.0053, r = 0.64), and baseline cholesterol (P = 0.037, r = 0.51). DATA CONCLUSION: The individual perfusion diabetes pattern score is a highly promising perfusion imaging biomarker for tracing the disease progression of individual T2DM patients. Further validation is needed from a larger study. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;49:834-844.