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In the present work, we report an update and extension of the previous ion-pair formation study of Hubers, M.M.; Los, J. Chem. Phys. 1975, 10, 235–259, noting new fragment anions from time-of-flight mass spectrometry. The branching ratios obtained from the negative ions formed in K + SF6 collisions, in a wide energy range from 10.7 up to 213.1 eV in the centre-of-mass frame, show that the main anion is assigned to SF5− and contributing to more than 70% of the total ion yield, followed by the non-dissociated parent anion SF6− and F−. Other less intense anions amounting to <20% are assigned to SF3− and F2−, while a trace contribution at 32u is tentatively assigned to S− formation, although the rather complex intramolecular energy redistribution within the temporary negative ion is formed during the collision. An energy loss spectrum of potassium cation post-collision is recorded showing features that have been assigned with the help of theoretical calculations. Quantum chemical calculations for the lowest-lying unoccupied molecular orbitals in the presence of a potassium atom are performed to support the experimental findings. Apart from the role of the different resonances participating in the formation of different anions, the role of higher-lying electronic-excited states of Rydberg character are noted.

This research presents an intelligent, environment-aware navigation framework for smart electric vehicles (EVs), focusing on multi-agent mapping, real-time obstacle recognition, and adaptive route optimization. Unlike traditional navigation systems that primarily minimize cost and distance, this research emphasizes how EVs perceive, map, and interact with their surroundings. Using a distributed mapping approach, multiple EVs collaboratively construct a topological representation of their environment, enhancing spatial awareness and adaptive path planning. Neural Radiance Fields (NeRFs) and machine learning models are employed to improve situational awareness, reduce positional tracking errors, and increase mapping accuracy by integrating real-time traffic conditions, battery levels, and environmental constraints. The system intelligently balances delivery speed and energy efficiency by dynamically adjusting routes based on urgency, congestion, and battery constraints. When rapid deliveries are required, the algorithm prioritizes faster routes, whereas, for flexible schedules, it optimizes energy conservation. This dynamic decision making ensures optimal fleet performance by minimizing energy waste and reducing emissions. The framework further enhances sustainability by integrating an adaptive optimization model that continuously refines EV paths in response to real-time changes in traffic flow and charging station availability. By seamlessly combining real-time route adaptation with energy-efficient decision making, the proposed system supports scalable and sustainable EV fleet operations. The ability to dynamically optimize travel paths ensures minimal energy consumption while maintaining high operational efficiency. Experimental validation confirms that this approach not only improves EV navigation and obstacle avoidance but also significantly contributes to reducing emissions and enhancing the long-term viability of smart EV fleets in rapidly changing environments.

In the present work, we report an update and extension of the previous ion-pair formation study of Hubers, M.M.; Los, J. , , 235-259, noting new fragment anions from time-of-flight mass spectrometry. The branching ratios obtained from the negative ions formed in K + SF collisions, in a wide energy range from 10.7 up to 213.1 eV in the centre-of-mass frame, show that the main anion is assigned to SF and contributing to more than 70% of the total ion yield, followed by the non-dissociated parent anion SF and F . Other less intense anions amounting to <20% are assigned to SF and F , while a trace contribution at 32u is tentatively assigned to S formation, although the rather complex intramolecular energy redistribution within the temporary negative ion is formed during the collision. An energy loss spectrum of potassium cation post-collision is recorded showing features that have been assigned with the help of theoretical calculations. Quantum chemical calculations for the lowest-lying unoccupied molecular orbitals in the presence of a potassium atom are performed to support the experimental findings. Apart from the role of the different resonances participating in the formation of different anions, the role of higher-lying electronic-excited states of Rydberg character are noted.

We report an ab initio and master-equation investigation of the gas-phase reactions of thioformaldehyde (H2CS) with CN and CCH radicals, motivated by the recent detection of the S-containing species HCSCN and HCSCCH in cold interstellar environments. Structures and frequencies were obtained at the DSD-PBEP86/aug-cc-pVTZ level, with energetics refined by CCSD(T)-F12a calculations and kinetics treated using an energy-grained master equation. The CN + H2CS reaction proceeds through orientation-dependent entrance channels. Two barrierless addition pathways lead to a connected multi-well network that preferentially forms cyano thioformaldehyde, HCSCN + H, whereas abstraction-type channels leading to HNC + HCS or HCN + HCS contribute only marginally. The calculated kinetics indicate capture-controlled low-temperature reactivity and a strong preference for HCSCN formation, suggesting that this reaction should be considered in astrochemical models of cold clouds. For CCH + H2CS, barrierless capture gives access to two connected entrance adducts. Although the cyclic product is the most exothermic channel, its formation is kinetically hindered by a high-lying late transition state. The flux is shared between propynethial formation, HCSCCH + H, and the HCCH + HCS channel, with the latter being favored. These results show that subtle differences in radical structure, bonding preferences, and entrance-channel topology strongly affect product branching in S-containing radical-molecule reactions. The computed mechanisms and rate coefficients provide useful input for astrochemical models of sulfur chemistry in cold molecular clouds and for interpreting recent molecular detections in sources such as TMC-1.

Propyl cyanide (PrCN) (C3H7CN) with both linear and branched isomers is ubiquitous in interstellar space and is important for astrochemistry as it is one of the most complex molecules found to date in the interstellar medium. Furthermore, it is the only one observed species to share the branched atomic backbone of amino acids, some of the building blocks of life. Radical-radical chemical reactions are examined in detail using density functional theory, second order Mller Plesset perturbation theory, coupled cluster methods, and the energy resolved master equation formalism to compute the rate constants in the low pressure limit prevalent in the ISM. Quantum chemical studies are reported for the formation of propyl-cyanide (n-PrCN) and its branched isomer (iso-PrCN) from the gas phase association and surface reactions of radicals on a 34-water model ice cluster. We identify two and three paths for the formation of iso-PrCN, and n-PrCN respectively. The reaction mechanism involves the following radicals association: CH3CHCH3+CN, CH3+CH3CHCN for iso-PrCN formation and CH3CH2+CH2CN, CH3+CH2CH2CN, CN+CH3CH2CH2 leading to n-PrCN formation. We employ the M062X/6-311++G(d,p) DFT functional and MP2/aug-cc-pVTZ for reactions on the ice model, and gas phase respectively to optimize the structures, compute minimum energy paths and zero-point vibrational energies of all reaction mechanisms. In gas phase, the energetics of the five reactions are also calculated using the explicitly correlated cluster ab initio methods (CCSD(T)-F12). All reaction paths are exoergic and barrier-less in gas phase and on the ice-model suggesting that the formation of iso-PrCN and n-PrCN is efficient on the water-ice model adopted in this paper. The gas phase formation of iso-PrCN and n-PrCN however requires a third body or spontaneous emission of a photon in order to stabilize the molecules.

This research focuses on enhancing the safety, reliability, and performance of IoT devices by optimizing the vibration characteristics of materials and noise control. We analyze materials’ vibration-damping properties to minimize mechanical resonance and ensure stable operation. By evaluating stiffness and resistance to deformation under dynamic stress, we examine the impact of vibration modulus on device reliability. Our study explores how damping and modulus influence vibrational energy propagation, noise reduction, and acoustic clarity. To integrate domain knowledge with real-time data, we develop interpretable methods that provide actionable insights into the mechanical-acoustic relationship. Compared with other established IoT security assessment techniques, this method has more effectiveness and superiority. Hybrid materials combining elastic matrices with rigid reinforcements are developed to fine-tune mechanical and acoustic properties for IoT applications, such as industrial systems or wearable devices. Vibration analysis is applied to predict performance under real-world conditions, improving safety and efficiency. Efforts are directed toward reducing vibrational noise and enhancing sound transmission for devices like smart speakers and voice recognition systems, ensuring a better user experience and greater functional accuracy.