Minimum State Research Articles

Constant advance replicas method for locating minimum energy paths and transition states.

The chain-of-states (CoS) constant advance replicas (CAR) method and its climbing image variant (CI-CAR) for locating minimum energy paths (MEPs) and transition states are reported. The CAR algorithm applies the Lagrange multiplier method for imposing holonomic constraints on a chain-of-replicas, aiming to maintain equal mass-weighted/scaled root-mean-square (RMS) distances between the adjacent replicas by removing the sliding-down displacements contributed by the potential gradients during path optimization. Two contextual regularization schemes with clear geometrical interpretations are implemented to jointly promote high convergence and numerical robustness of the CAR algorithm. We show that the constrained reaction path can be solved normally within 5 steps of Lagrange multiplier updates with remarkably high numerical precision via the CAR approach. The efficacy of the CAR methods is demonstrated by testing on multiple analytical, classical, and quantum mechanical transition paths: the Müller potential, the alanine dipeptide isomerization, the helix unwinding of the VIVITLVMLKKK 12-mer peptide, and the Baker set of reactions. We also explore the potential of applying adaptive momentum (AdaM) optimizers for locating optimal transition paths under complex conformational changes. Most importantly, we discuss extensively the differences and connections between our newly proposed CAR methods and several related methods, with focuses on the reaction path with holonomic constraints (RPCons) approach of Brokaw et al. [J. Chem. Theory Comput. 2009, 5 (8), 2050-2061] and the state-of-the-art string method (SM) of E et al. [J. Chem. Phys. 2007, 126 (16), 164103]. The CAR approach represents a latest update to the general theoretical framework of reaction path finding algorithms in the two-ended CoS regime.

Synthesis and characterization of tailor-made o-hydroxysubstituted anils through 1H NMR, ­13C NMR, SC-XRD and DFT studies for possible optoelectronic applications

The present work reports the synthesis of two simple tailor-made anils (o-hydroxybezylidene-orthochloroaniline & o-hydroxynaphthalidene-orthochloroaniline) and their characterization by 1H & 13C Nuclear magnetic resonance(NMR), single X-ray diffraction spectroscopy(SC-XRD) and DFT studies. DFT-B3LYP optimized the molecular structures with a 6-311G (d, p) basis set to accomplish the global minimum energy state. 1H NMR confirmed the occurrence of H-bonding between the phenolic hydrogen and azomethine nitrogen in both molecules, further supported by SC-XRD analysis and DFT studies. The structural parameters such as bond angle, bond length, and dihedral angle derived from the optimized structure of molecules with C1 symmetry were compared with the corresponding parameters extracted from the ORTEP diagrams for both anils. Appreciable harmony of the structural parameters was achieved between these two analyses, confirming the anils’ purity. Slight discrepancy observed between DFT and SC-XRD analyses was attributed to the difference in the state of the molecules. Geometric optimization indicated the molecules to be coplanar. The MEP surface and atomic charges on the atoms envisaged the electron population and charged sites of the anils. Conclusively, both the anils have a suitable structural hierarchy for superior electron transport from the phenyl rings over the entire molecular framework suitable for optoelectronics applications.

Experimental techno-economic analysis of an autonomous photovoltaic system operating in Chocó, Colombia

Despite of scientific efforts to evaluate economically the photovoltaic plants integrating the costs generated by the technical behavior of the system, few studies have demonstrated the behavior of the economic parameters from experimental data obtained from the photovoltaic plants in real-time operation. On the other hand, the cost analysis carried out by various authors breaks down the equation for calculating the Levelized cost of energy in independent terms without considering the marketing options of local markets in remote regions.This article discusses the results of a lead–acid battery system monitored over six years of operation. The system is part of a 20 kWp independent, modular photovoltaic power plant and is divided into two parallel circuits, each consisting of 24 batteries. Technical parameters such as state of charge, Pb-A system voltage, temperature, and charge/discharge currents were monitored, along with power flow before and after the Pb-A system inverter. The incremental charge/discharge efficiency between two parallel circuits, the duration of the charge and discharge cycles, and the Coulombic efficiency were evaluated from the measured data. A statistical analysis of the state of charge was performed to specify the energy delivered from the series of battery banks. This allowed for an evaluation and understanding of the operational performance of the photovoltaic system from a technical and economic point of view. The average minimum and maximum state of charge for bank 1 was 78.9% and 99.96%, respectively, and for bank 2 was 78.15% and 99.83%, respectively, for the monitored period. The efficiency of the incremental cycle varies for Sb1 from 92% to 99% and for Sb2 between 80% and 99%. From the monitoring of these parameters and using actual costs, the Levelized Cost of Electricity was determined for various discount rates.

A comparison of modified extremal optimization algorithm vs quenching for electron glass system optimization

Coulomb glass or electron glass refers to a system of localized electrons interacting via unscreened Coulomb interactions. The electrons are localized on a square lattice site, and the site's energies are random. This model was mainly used to represent amorphous and compensated semiconductors at high disorders and low temperatures. Recently, the model has been used to model granular systems, an array of nanocrystals, and graphene. In this work, we have obtained the ground state of an electron glass model in two dimensions using a modified extremal optimization. The work is done for disorders less than the critical disorder and at half-filling. For this case, the ground state has anti-ferromagnetic ordering, unlike the high disorder case. This allows us to check the efficiency of our new algorithm.The results are compared with quenching using Monte Carlo simulation. In the electron glass model, the number of electrons is conserved, and the interaction is long-ranged. Extremal optimization was developed to obtain the ground state of short-ranged spin systems with a non-conserved magnetization. The algorithm was extended to find the ground state of a long ranged anti-ferromagnet with conserved magnetization. We show that the efficiency of extremal optimization is comparable to the quenching method. The efficiency for both ways decreases as the disorder is increased. The minimum energy states obtained consist of mainly two large domains when the ground state was not achieved. These meta-stable states are stable, and increasing the run time increases the efficiency only by a small amount. The stability of the domains is analyzed using an activity graph. A comparison of the activity graphs for the same disorder configuration for the two algorithms shows that the formation of a meta-stable state is a general feature independent of the algorithm used. The formation of domain states is more prevalent for specific disorder configurations. These results can be explained in terms of domain formation theory.

Theoretical and Numerical Investigation of Reduction of Viscous Friction in Circular and Non-Circular Journal Bearings Using Active Lubrication

Reducing friction losses is one of the most common ways to improve fluid film bearings, whose adjustable design provides additional opportunities to improve their dynamic and tribological properties. Previous studies have shown the possibility of reducing viscous friction in actively lubricated bearings by adjusting the rotor position. This work provides a theoretical justification for the mechanism of this effect for the cases of purely laminar lubricant flows in journal bearings. The operating modes connected with the transition to turbulent phenomena and the occurrence of Taylor vortices are beyond the scope of this paper. Conditions that ensure the minimization of friction losses in hydrodynamic and hybrid bearings with hydrostatic parts are determined based on the equations describing viscous friction in a fluid film. In non-adjustable plain hydrodynamic bearings, the minimum of friction is achieved with the centered shaft position that is actually unstable due to the resulting forces configuration. In actively lubricated hybrid bearings, a further reduction in viscous friction is possible by combining film thickness and pressure distributions. Recombining them, along with adjustment of the shaft position, allows the optimization of the distribution of shear stresses in the fluid film. As a result, the shear stresses caused by the rotation of the shaft can be partially compensated by the stresses caused by the pressure gradient, reducing the torque-resisting rotation. In addition, additional benefits can be obtained in the minimum friction state by the reduced lubricant flow and power losses to its pumping. A series of numerical calculations for elliptical, 3-, and 4-lobe bearings show that non-circular bores provide additional variability in film thickness distribution and a premise for optimizing the bearing tribological parameters. Four-lobe bearing demonstrated the best ability for reducing viscous friction among the considered designs. The results obtained can be used as a basis for further optimization of the geometry of fluid film bearings of both active and passive designs by reducing power losses due to viscous friction.

Self-Assembly of DNA Homopolymers by Pathway Dependence to Evade Metastable States.

A pathway-dependent strategy is proposed to assist single-stranded DNA polyadenine (poly(dA)) in evading metastable states and to achieve morphological regulation from microcapsules to microbowls by fractional n-butanol addition and emulsification (shaking) in a soft emulsion template (water-in-n-butanol). The first stage is the formation of small microcapsules by a fourth solvent addition and shaking. The second stage is the expansion of the small microcapsules initiated by the fifth solvent addition and shaking, drawing them to a new pathway to evade metastable states. Osmotic re-equilibrium and shaking are two indispensable conditions for overcoming the energy barriers. The third stage is the buckling of the expanded microcapsules and the evolution into microbowls after the evaporation of n-butanol to reach a global free energy minimum stable state. Conversely, the conventional one-time solvent addition and shaking pathway do not obtain microbowls. This kinetics pathway-dependent strategy evades metastability and shapes DNA oligonucleotides into desired structures via self-assembly.

Property-Based Brittleness Analysis of Temporal Networks

A new framework is proposed to analyze the brittleness of task networks with respect to an arbitrary set of user-specified properties, such as dynamic controllability or minimum battery state of charge of a rover. The proposed algorithms allow the detection and enumeration of activities that, with modest task execution duration variation, make the success of execution no longer guaranteed in terms of the given set of properties. A metric for measuring an activity’s brittleness, defined as the degree of acceptable deviation from its nominal duration that preserves the desired user-specified properties, is introduced and how that measurement is mapped to task network structure is described. Complementary to existing work on temporal robustness analysis, which informs how likely a task network is to succeed or not under temporal controllability constraints, the new analysis and metric not only generalize robustness analysis to a set of user-defined properties (as opposed to strong or dynamic controllability, for instance), but they also go deeper to pinpoint the sources of potential brittleness. An analyzer is developed, which helps human designers and/or automated task network generators focus on and address sources of undesirable brittleness. The approach is applied in Mars rover and biomanufacturing operations scenarios.

Large ferroelectric photovoltaic effect of a wurtzite-structure SnC/ScN superlattice

A high bandgap is the most important factor impeding the development of ferroelectric photovoltaic (FE-PV) materials. Finding a new FE-PV material with a narrow bandgap is thus very urgent. Here, a wurtzite-structure SnC/ScN (w-SSCN) superlattice was constructed to obtain light absorption properties using first-principles calculations based on the narrow bandgap of group IV and their alloys. The mechanic and dynamic calculations show that w-SSCN is stable. It has a lower band gap (2.796 eV) and a larger visible absorption coefficient than traditional ferroelectric material BaTiO3, and is even close to Si. In addition, ferroelectric polarization switching can be achieved due to the small barrier (0.215 eV). The polarization decreases under tensile strain, but the visible light absorption is enhanced due to the decrease in the bandgap. Meanwhile, the switching barrier reduces. The w-SSCN with 4% tensile strain exhibits a remarkable visible light absorption property due to the narrow bandgap (1.216 eV), and its spontaneous polarization is relatively large (0.535 C/cm2) under a low switching barrier (0.011 eV). The density of states diagram shows that the conduction band minimum (CBM) state and the valence band maximum (VBM) state come from the Sn 5 s and C 2p states, respectively. The contributions of electrons and holes originating from the different atoms suppress recombination, which enhances the solar power conversion efficiency. This work provides a new way to promote the development of FE-PV materials.

Optimization of Coulomb glass system using quenching and annealing at small disorders

Abstract In this paper we have used Monte Carlo simulations to study the two dimensional Coulomb glassmodel at half filling. The simulations were carried out using the Metropolis algorithm at zero and small finite disorder. We have used two protocols namely quenching and annealing to find the ground state of the system. In the quenching protocol at zero disorder, we looked at the effect of temperature on the efficiency of finding the ground state. The efficiency does not improve by decreasing the temperature. Since efficiency for quenching protocol was very low, we then used the annealing protocol to find the minimum energy state of the system. The annealing protocol was much more efficient than the quenching procedure. At finite disorder less than the critical disorder, the efficiency of the quenching protocol decreased compared to the zero disorder case. These results have been discussed in terms of domain theory and the density of states.

Integration of battery and hydrogen energy storage systems with small-scale hydropower plants in off-grid local energy communities

The energy transition is pushing towards a considerable diffusion of local energy communities based on renewable energy systems and coupled with energy storage systems or energy vectors to provide independence from fossil fuels and limit carbon emissions. Indeed, the variable and intermittent nature of renewables make them inadequate to satisfy the end-users’ electricity demand throughout the whole day; thus, the study of energy storage systems, considering their seasonal storage behaviour (e.g., energy-power coupling, self-discharge loss, and minimum state of charge) is fundamental to guarantee the proper energy coverage. This work aims at identifying the off-grid operation of a local energy community powered by a 220 kW small-scale hydropower plant in the center of Italy using either a battery energy storage system or a hydrogen one with the Calliope framework. Results show that, whereas the hydrogen storage system is composed of a 137 kW electrolyser, a 41 kW fuel cell, and a storage of 5247 kgH2, a battery system storage system would have a capacity of 280 MWh. Even though the battery storage has a better round-trip efficiency, its self-discharge loss and minimum state of charge limitation involve a discharging phase with a steeper slope, thus requiring considerable economic investments because of the high energy-to-power ratio.

A Contractarian Approach to the Concept of a Minimal State

This article explores the concept of a minimum state from the perspective of social contractarianism. The minimal state, rooted in the classical liberal school of political philosophy, focuses on defending citizens' rights and liberties as its primary obligation. Advocates like John Locke, Thomas Hobbes, and John Stuart Mill argued that a government should maintain law and order, ensuring citizens live in an orderly society. The concept of the general will, where individuals jointly choose laws and policies, was pioneered by Rousseau. The Enlightenment era coincided with the emergence of the minimal state, as academics and thinkers questioned the absolute power of monarchs and hierarchical structures.

Electricity peak shaving for commercial buildings using machine learning and vehicle to building (V2B) system

Reducing electricity peak demand is essential to maintain the balance between supply and demand side in electricity power markets, as well as reduce utility costs and environmental impacts. With growth in adoption of Electric Vehicles (EVs), there is an emerging opportunity to balance electrical power demand of buildings by storing electricity in EVs during low demand periods and discharging electricity into buildings during peak demand periods. Due to uncertainty in time and magnitude of peak demand, decision makers are always faced with a challenging task to identify optimal schedules for charging and discharging EVs to minimize peak electricity demand. This paper presents the development of a novel system that is capable of predicting day-ahead building electricity demand profile and identifying optimum schedule of charging and discharging EVs to minimize electricity peak demand. The system is designed to comply with planned EV trip schedules and minimum state of charge (SOC). The system consists of (1) machine Learning (ML) model to predict electrical power demand, and (2) demand management optimization model to identify optimal schedule for charging and discharging EVs. Four methods are explored to develop the ML model, including histogram-based gradient boosting, random forest, deep artificial neural network (DNN), and long short-term memory (LSTM). A case study of multi-tenant commercial building is analyzed to evaluate the performance of the system and demonstrate its new capabilities. The results of the case study shows that LSTM has the best performance in terms of mean absolute error, root mean square error, and mean absolute percentage error with average values of 7.44, 17.78, and 20.08 %, respectively. Five scenarios for shaving peak electricity demand, including combinations of two electric vehicles, a stationary battery, and a PV system are investigated. Scenarios including the stationary battery and the PV system are considered to evaluate the full potential of peak demand reduction in the case study building. The results of the demand management optimization model show up to 36 % reduction in peak demand using two EVs, one stationary battery, and PV system of 40 kW capacity. The key contributions that this study adds to existing knowledge are: (1) developing machine learning models to predict day-ahead electricity demand in 15-minute intervals, (2) integrating machine learning and optimization algorithms in identifying EV charging and discharging schedules to minimize utility cost by shaving peak energy demand, and (3) considering planned EV trips and minimum SOC requirements in identifying optimal charging and discharging of EVS to shave peak energy demand in buildings. The implementation of this system provides practical solutions for managing electricity demand in commercial buildings using EVs. By reducing energy consumption and promoting the innovative use of EVs, this system offers a sustainable approach to managing electricity demand in commercial buildings.

Uncertainty relation for non-Hermitian systems

We construct an uncertainty relation for arbitrary finite-dimensional $\mathcal{PT}$-invariant non-Hermitian quantum systems within a special inner product framework. This construction is led by ``good observables,'' which are a more general class of operators. We show that the cumulative gain in the quantum Fisher information when measuring two good observables for such non-Hermitian systems is much better than their Hermitian counterpart. Minimum uncertainty states being the best candidates for this gain near the exceptional point supports the intelligent or simultaneous non-Hermitian quantum sensors.

A predictive control strategy based on A-ECMS to handle Zero-Emission Zones: Performance assessment and testing using an HiL equipped with vehicular connectivity

Recently, several metropolitan cities introduced Zero-Emissions Zones where the use of the Internal Combustion Engine is forbidden to reduce localized pollutants emissions. This is particularly problematic for Plug-in Hybrid Electric Vehicles, which usually work in depleting mode. So, the risk of not having enough energy stored to carry out the driving mission and then paying a fee is substantial. This work presents a viable solution by exploiting vehicular connectivity to retrieve navigation data of the urban event along a selected route. The battery energy needed, in the form of a minimum State of Charge (SoC), is calculated by a Speed Profile Prediction algorithm and a Backward Vehicle Model. That value is then fed to both a Rule-Based Strategy, developed specifically for this application, and an Adaptive Equivalent Consumption Minimization Strategy (A-ECMS). The effectiveness of this approach has been tested with a Connected Hardware-in-the-Loop (C-HiL) on a driving cycle measured on-road, stimulating the predictions with multiple re-routings. The tests have been conducted with different initial SoC values for each strategy, showing a maximum error in the SoC prediction of 2.4% and up to 26.1% of CO2 saving with the A-ECMS.

An Efficient Combined Bit-Width Reducing Method for Ising Models

Annealing machines such as quantum annealing machines and semiconductor-based annealing machines have been attracting attention as an efficient computing alternative for solving combinatorial optimization problems. They solve original combinatorial optimization problems by transforming them into a data structure called an Ising model. At that time, the bit-widths of the coefficients of the Ising model have to be kept within the range that an annealing machine can deal with. However, by reducing the Ising-model bit-widths, its minimum energy state, or ground state, may become different from that of the original one, and hence the targeted combinatorial optimization problem cannot be well solved. This paper proposes an effective method for reducing Ising model's bit-widths. The proposed method is composed of two processes: First, given an Ising model with large coefficient bit-widths, the shift method is applied to reduce its bit-widths roughly. Second, the spin-adding method is applied to further reduce its bit-widths to those that annealing machines can deal with. Without adding too many extra spins, we efficiently reduce the coefficient bit-widths of the original Ising model. Furthermore, the ground state before and after reducing the coefficient bit-widths is not much changed in most of the practical cases. Experimental evaluations demonstrate the effectiveness of the proposed method, compared to existing methods.

Morphodynamic Resilience of the Tide‐Dominated Estuary With Interference From Tidal Flat Reclamations

AbstractMost tide‐dominated estuaries worldwide are among the morphodynamic shrinking types heavily disturbed by anthropogenic interventions like embankments and reclamations. A typical example is North Branch (NB), the northern first‐order distributary of Yangtze (Changjiang) Estuary, showed a rapid alteration from river‐dominated to tide‐dominated character in less than 50 years after intensive reclamations, resulting in serious morphodynamic shrinkage, thus attracted great concern of its evolutionary fate. Here, we explored this issue based on historical bathymetry data and hydro‐morphodynamic modeling to examine the morphology‐hydrology interactions and the suggested equilibrium re‐adjustment mechanism after intensive reclamations. Results demonstrated a mechanism of geomorphological resilience for the tide‐dominated estuary, evolving from preliminary estuarine shrinking to dynamic equilibrium after reclamations. Although reclamation could destabilize the system, leading to temporary positive feedback between tidal pumping and morphology shrinking, driving the estuary vanishing quickly in a short time. A substantial loss in the intertidal areas, changing the estuary geometry from a highly‐curved bank to a highly‐constrained channel, weakened flood tidal asymmetry of NB, which in turn altered sediment dynamics from deposition to erosion. Such negative feedback process tends to drive the estuary toward a new dynamic equilibrium morphology. The discovered nonlinear feedback mechanism is supported by the evidence of system efficiency adjustment, which was substantially gained during the first 30 years, then stayed in a low entropy production and minimum work state, indicating a more efficient dynamic equilibrium. If further estuary preservation policies are implemented, the dynamic equilibrium of NB is expected to continue for a long time in the future.

Extensor Mechanism Disruption Remains a Challenging Problem

BackgroundExtensor mechanism disruption (EMD) following total knee arthroplasty (TKA) is a devastating problem commonly treated with allograft or synthetic reconstruction. Understanding of reconstruction success rates and patient recorded outcomes is lacking. MethodsPatients who have an EMD after TKA undergoing mesh or whole-extensor allograft reconstruction between 2011 and 2019, with minimum 2-year follow-up were reviewed at two tertiary care centers. Functional failure was defined as extensor lag >30 degrees, amputation, or fusion, as well as revision extensor mechanism reconstruction (EMR). Survivorship was assessed using Kaplan–Meier curves, and factors for success were determined with logistic regressions. ResultsOf fifty-six EMRs (49 patients), 50.0% (28/56) were functionally successful at 3.2 years of mean follow-up (range, 0.2 to 7.4). In situ survivorship of the reconstructions at 36 months was 75.0% (42 of 58). There were 50.0% (14 of 28) of functionally failed EMRs that retained their reconstruction at last follow-up. Mean extensor lag among successes and failures was 5.4 and 71.0° (P = .01), respectively. Mean Knee Injury and Osteoarthritis Outcome Score, Joint Replacement scores were 67.1 and 48.8 among successes and failures (P = .01). There were 64.0% (16 of 25) of successes and 1 of 19 failures that obtained a Knee Injury and Osteoarthritis Outcome Score, Joint Replacement score above the minimum patient-acceptable symptom state for TKA. Survivorship and success rates were similar between reconstruction methods (P = .86; P = .76). All-cause mortality was 8.2% (4 of 49), each with EMR failure prior to death. All-cause reoperation rate was 42.9% (24 of 56), with a 14.3% (8 of 56) rate of revision EMR and 10.7% (6 of 56) rate of above-knee-amputation or modular fusion. ConclusionsThis multicenter investigation of mesh or allograft EMR demonstrated modest functional success at 3.2 years. Complication and reoperation rates were high, regardless of EMR technique. Therefore, EMD after TKA remains problematic.

Optimization of grid-photovoltaic and battery hybrid system with most technically efficient PV technology after the performance analysis

The performance assessment of renewable energy technologies, such as PV systems, is pivotal in planning for hybrid energy systems. This work clears the way for researchers to construct the best PV-based hybrid systems by first performing performance analysis metrics suggested by IEC 61724 on several PV technology options, and then choosing the most technically sound option. Next, using HOMER Pro software, construct a PV/grid and battery hybrid system with effective PV technology. The performance of three PV technologies, i.e., monocrystalline silicon, polycrystalline silicon, and amorphous silicon is assessed to demonstrate how their performance varies depending on the location. Results showed that (m-Si) has greater performance than the other two PV technologies, with yearly array yields of 5.26 h/day, reference yields of 6.18 h/day, and final yields of 5.18 h/day, a capacity factor of 14.31%, system losses of 0.076, and a performance ratio (PR) of 83.9%. The most effective configuration of the system under evaluation consists of a 72.8 kW PV connected to the grid and a 59.4 kW converter. The total NPC is expected to be Rs. $397,684 for the optimal configuration, costing Rs. $0,0493/kWh. The additional electricity in the best situation is 977 kWh, which is insignificant compared to the system capacity. Two sensitivity variables, including changes in the main load, are anticipated to be 10% and 15% higher than the current value (1698.63 kWh) and the battery's minimum state of charge, with 20%, 25%, and 30% uncertainty, are addressed.

DFT and TD-DFT study of hydrogen bonded complexes of aspartic acid and n water (n = 1 and 2)

Hydrogen bonds (HB) influence the conformational preferences of biomolecules and their optical and electronic properties. The directional interaction of molecules of water can be a prototype to understand the effects of HBs on biomolecules. Among the neurotransmitters (NT), L-aspartic acid (ASP) stands out due to its importance in health and as a precursor of several biomolecules. As it presents different functional groups and readily forms inter- and intramolecular HBs, ASP can be considered a prototype for understanding the behavior of NTs when interacting by HB with other substances. Although several theoretical studies have been performed in the past on isolated ASP and its formed complexes with water, both in gas and liquid phases, using DFT and TD-DFT formalisms, these works did not perform large basis set calculations or study electronic transitions of ASP-water complexes. We investigated the HB interactions in complexes of ASP and water molecules. The results show that the interactions between the carboxylic groups of ASP with water molecules, forming cyclic structures with two HBs, lead to more stable and less polar complexes than other conformers formed between water and the NH2 group. It was observed that there is a relationship between the deviation in the UV-Vis absorption band of the ASP and the interactions of water with the HOMO and LUMO orbitals with the stabilization/destabilization of the S1 state to the S0 of the complexes. However, in some cases, such as 1:1 complex ASP-W2, this analysis may be inaccurate due to small changes in ΔE. We studied the landscapes of the ground state surface of different conformers of isolated L-ASP and the L-ASP-(H2O)n complexes (n = 1 and 2) using the DFT formalism, with the B3LYP functional, and six different basis sets: 6-31 + + G(d,p), 6-311 + + G(d,p), D95 + + (d,p), D95V + + (d,p), cc-pVDZ, and, cc-pVTZ basis sets. The cc-pVTZ basis set provides the minimum energy of all conformers, and therefore, we performed the analysis with this basis set. We evaluated the stabilization of the ASP and complexes using the minimum ground state energy, corrected by the zero point energy and the interaction energy between the ASP and the water molecules. We also calculated the vertical electronic transitions S1 ← S0, and their properties using the TD-DFT formalism at B3LYP/cc-pVTZ level with the optimized geometries for S0 state with the same basis set. For the analysis of the vertical transitions of isolated ASP and the ASP-(H2O)n complexes, we calculated the electrostatic energy in the S0 and S1 states. We performed the calculations with the Gaussian 09 software package. We used the VMD software package to visualize the geometries and shapes of the molecule and complexes.

Diving into the magnetosphere of the Of?p star HD 108

ABSTRACT We analyse optical and X-ray spectroscopy of the Of?p star HD 108, known for its strong dipolar magnetic field and its optical line profile variability with a time-scale of 54 ± 3 yrs, interpreted as the stellar rotation period. Optical emission lines have now recovered from their minimum emission state reached in 2007–2008. The variations of the equivalent width of the Hα emission provide constraints on the inclination of the rotation axis (i) and the obliquity of the magnetic axis (β). The best agreement between model and observations is found for (i, β) pairs with i + β ≃ 85° and i ∈ [30°, 55°]. The Balmer emission lines display stochastic variability at the ∼5 per cent level on time-scales of a few days. TESS photometry unveils transient modulations on similar time-scales in addition to prominent red noise variations. A Chandra X-ray observation of December 2021, when the star was at a higher emission level, indicates a slight increase of the flux and a spectral hardening compared to the August 2002 XMM-Newton observation, taken near minimum emission state. Magnetohydrodynamic simulations are used to compute synthetic X-ray spectra. With our current best estimate of the $\dot{M}_{B=0}$ mass-loss rate, the simulated X-ray luminosity and spectral energy distribution agree very well with the observations. Finally, the radial velocities vary on a period of 8.5 yr with a peak-to-peak amplitude of 10–11 km s−1, suggesting orbital motion with an unseen companion of at least 4 M⊙.