Excited anthracene is well-known to photodimerize and not to exhibit excimer emission in isotropic organic solvents. Anthracene (AN) forms two types of supramolecular host−guest complexes (2:1 and 2:2, H:G) with the synthetic host octa acid in aqueous medium. Excitation of the 2:2 complex results in intense excimer emission, as reported previously, while the 2:1 complex, as expected, yields only monomer emission. This study includes confirming of host−guest complexation by NMR, probing the host− guest structure by molecular dynamics simulation, following the dynamics AN molecules in the excited state by ultrafast time-resolved experiments, and mapping of the excited surface through quantum chemical calculations (QM/MM-TDDFT method). Importantly, time-resolved emission experiments revealed the excimer emission maximum to be time dependent. This observation is unique and is not in line with the textbook examples of time-independent monomer−excimer emission maxima of aromatics in solution. The presence of at least one intermediate between the monomer and the excimer is inferred from time-resolved area normalized emission spectra. Potential energy curves calculated for the ground and excited states of two adjacent anthracene molecules via the QM/MM-TDDFT method support the model proposed on the basis of time-resolved experiments. The results presented here on the excited-state behavior of a well-investigated aromatic molecule, namely the parent anthracene, establish that the behavior of a molecule drastically changes under confinement. The results presented here have implications on the behavior of molecules in biological systems.

LiF–NaF–ZrF4 multicomponent molten salts are identified as promising candidates for coolant salts in molten salt reactors and advanced high-temperature reactors. This study focused on low-melting point salt compositions of interest: 38LiF–51NaF–11ZrF4, 42LiF–29NaF–29ZrF4, and 26LiF–37NaF–37ZrF4. Ab-initio molecular dynamics (AIMD) calculations were performed and compared with available experimental data to assess the ability of rigid ion models (RIM) to reproduce short- to intermediate-range structure, transport, and thermophysical properties of the LiF–NaF–ZrF4 salt mixtures. It is found that as ZrF4 mol% increases, the average cation–anion coordination number (CN) of monovalent cations (Li+, Na+) obtained from RIM calculations decreases, while multivalent Zr4+ CN varied from 15% to 19% in comparison to corresponding AIMD values. In addition, RIM is found to predict the existence of 7, 8, and 9 coordinated fluorozirconate complexes, while AIMD and the available experimental data showed an occurrence of 6, 7, and 8 coordinated complexes in the melt. The intermediate-range structure analysis revealed that while the RIM parameters are able to reproduce a local structure for lower ZrF4 mol% salts such as in 38LiF–51NaF–11ZrF4, an extensive fluorozirconate network formation is observed in RIM simulations for higher ZrF4 mol% compositions. The network generated by RIM parameters is found to be mainly connected by “corner-sharing” fluorozirconate complexes as opposed to both “edge-sharing” and “corner-sharing” connectively portrayed by AIMD. It is found that a close agreement between AIMD and the RIM salt structure for the 11-mol% ZrF4 salt resulted in good agreement in the calculated Zr diffusivities and the viscosity values. However, due to the inaccurate short- to intermediate-range structure prediction by RIM for higher ZrF4 mol% compositions, thermophysical properties such as densities and heat capacity differ by up to 26% and 27%, respectively, upon comparison with AIMD and experimental values. Also, the network-dominated properties such as diffusion coefficients and viscosities differed by up to two and three orders of magnitude, respectively. This study signifies the importance of accurate salt structure generation for an accurate prediction of transport and thermophysical properties of multicomponent molten salts.

LiF−NaF−ZrF 4 multicomponent molten salts are promising candidate coolants for advanced clean energy systems owing to their desirable thermophysical and transport properties. However, the complex structures enabling these properties, and their dependence on composition, is scarcely quantified due to limitations in simulating and interpreting experimental spectra of highly disordered, intermediate-ranged structures. Specifically, sizelimited ab initio simulations and accuracy-limited classical models used in the past are unable to capture a wide range of fluctuating motifs found in the extended heterogeneous structures of liquid salt. This greatly inhibits our ability to design tailored compositions and materials. Here, accurate, efficient, and transferable machine learning potentials are used to predict structures far beyond the first coordination shell in LiF−NaF−ZrF 4 . Neural networks trained at only eutectic compositions with 29% and 37% ZrF 4 are shown to accurately simulate a wide range of compositions (11−40% ZrF 4 ) with dramatically different coordination chemistries, while showing a remarkable agreement with theoretical and experimental Raman spectra. The theoretical Raman calculations further uncovered the previously unseen shift and flattening of bending band at ∼250 cm −1 which validated the simulated extended-range structures as observed in compositions with higher than 29% ZrF 4 content. In such cases, machine learning-based simulations capable of accessing larger time and length scales (beyond 17 Å) were critical for accurately predicting both structure and ionic diffusivities.

—Energy Meter is widely used a technology. It can be seen in everywhere. It can be found worldwide. It is useable in house or any industrial place also. Generally, it measures all the power consumed by the consumer & it gives the total consumption till that date by the consumer. The main problem is that the consumer can’t able to calculate how much he/she is consuming & also can’t reduce their misuse. This device will check current consumption & it will store data on basis of daily, monthly & yearly. By this way consumer can understand how much they are consuming & how much they are misusing. Also, they can make a statistic that how much they have consumed in last year summer & in present summer. As we are going to implement IoT it is going to show their consumption & it will give the whole data in excel sheet. Also, in excel sheet it will generate chat & bar diagram where consumption statistics will be clear to all consumers. IoT will help them to generate all data whenever it is needed. By this way they can stop their current m is use.