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Double transition metal (DTM) nitride MXenes offer enhanced electrical conductivity and tunable catalytic properties compared to conventional carbide-based MXenes. In this study, we employed first-principles density functional theory (DFT) calculations to discover and computationally validate a novel DTM nitride MXene, Nb2TiN2, derived from its MAX phase precursor and investigated its potential as an anchoring material (AM) for Li–Se batteries. This newly proposed MXene expands the compositional landscape of DTM nitrides and opens new avenues for functional material design. We performed a comprehensive analysis of the thermodynamic and electronic properties of Nb2TiAlN2, and the MAX phase precursor to Nb2TiN2 to assess its structural stability and exfoliation potential. Exfoliation energy calculations confirmed the feasibility of synthesizing Nb2TiN2 from Nb2TiAlN2. We then explored the functionalized form, Nb2TiN2S2, evaluating its capability to serve as an effective anchoring material (AM) in Li–Se batteries by analyzing the reaction mechanisms and kinetics of the selenium reduction reaction (SeRR). Our results indicate that Nb2TiN2S2 exhibits a strong binding affinity for lithium polyselenides (Li2Sen), effectively suppressing the shuttle effect. Gibbs free energy calculations for the rate-limiting step of the SeRR reveal favorable kinetics and reduced reaction barriers. Overall, this study provides a detailed evaluation of the structural and electronic properties of a newly proposed DTM nitride MXene and its S-functionalized derivative and the catalyzing effect of Nb2TiN2S2 in accelerating the reaction kinetics in Li–Se batteries. These findings underscore the potential importance of the further exploration of MXenes to address current challenges in high-performance Li–Se batteries. 

As next-generation wireline and wireless systems are scaled to meet increasing data demands, existing signal processing approaches face significant power and latency challenges. To address these demands, we present CAMEL (Capacitive Analog In-Memory Equalization), a mixed-signal, discrete-time, analog in-memory switched-capacitor finite impulse response (FIR) filter designed in Intel16. Using this filter as a core, we develop a 16-tap antenna-domain I/Q equalizer, with 8-bit accuracy, consuming 90 mW from a 1 V supply, while achieving a data rate of 2 Gbps at a bit error rate (BER) of 10−4 in a realistic channel at 18 dB signal-to-noise ratio (SNR). Mismatch analysis and scaling studies indicate that this design can be extended to 12 bit and 48-tap configurations with linear increase in power, while delivering full digital reconfigurability, and datarates exceeding 5 Gbps with a power efficiency of 9.81 pJ/bit. 

Thermal ablation of materials is a complex phenomenon that involves physical and chemical processes for the thermal protection of systems. However, due to the extreme thermal conditions and moving boundaries, predicting temperature and heat flux at the ablative material is quite challenging. A physics-informed neural network is a promising technique for many such inverse problems, including the prediction of unsteady heat flux. However, traditional physics-informed machine learning algorithms struggle with heat flux predictions in thermal ablation problems due to moving boundary conditions and lack of temperature data in the inaccessible domain. This study presents a hybrid approach, where an artificial neural network (ANN) is used for the accessible domain of the material and a physics-based numerical solution (PNS) technique is used in the inaccessible domain of the material, to find heat flux at the ablative surface. Temperature data at the accessible sensor points are used to train the ANN model. The heat flux at the ablative boundary was iteratively obtained from the numerical solution of the energy equation in the inaccessible domain by matching the ANN-predicted temperature at the last accessible sensor point. Our results indicate that this hybrid methodology significantly outperforms traditional physics-informed machine learning techniques, achieving excellent accuracy in predicting the temperature profiles and heat fluxes under complex conditions for both constant and variable heat flux and properties. By addressing the limitations of conventional physics-informed machine learning methods, our approach provides a robust and reliable solution for modeling the intricate dynamics of ablative processes. 

ABSTRACT As modern agriculture faces increasing demands for efficiency and automation, this study presents a novel, untethered soft gripper system designed for autonomous and efficient harvesting. At the core of this innovation is a piston‐driven, pneumatically actuated gripper embedded with flexible tactile sensors, enabling operation without an external air source. The system integrates a compact motorized syringe, forming a closed‐loop fluid circuit that provides precise pressure control for adaptive grasping. The pneumatic actuation mechanism regulates air pressure from −30 to 180 kPa, allowing the gripper to perform delicate and adaptive handling, particularly suited for tree fruits and other fragile crops. A key feature of the system is its intelligent control mechanism, which seamlessly combines pneumatic and electrical systems to enhance autonomy and versatility in agricultural applications. The integration of size recognition and adaptive grasping, enabled by force feedback from embedded tactile sensors, ensures safe, efficient, and damage‐free harvesting. Demonstrating exceptional potential for autonomous agricultural operations, the untethered soft gripper system offers enhanced independence, maneuverability, and adaptability across diverse harvesting environments. Its ability to optimize crop handling while minimizing damage highlights its significance as a pioneering solution for the future of automated agriculture.