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Graphene Field-Effect Transistors are increasingly employed as biochemical sensors due to their exceptional electronic properties, surface sensitivity, and potential for miniaturization. A critical challenge in deploying these platforms is determining the optimal electrical readout strategy. They are typically operated with either of two modalities: one measuring current in real time (amperometric) and the other monitoring the change in voltage for charge neutrality (potential potentiometric). Here, we undertake a systematic study of the two modalities to determine their relative advantages/disadvantages towards guiding the future use of graphene field-effect transistors in sensing. We focus on viral proteins in wastewater, given the matrix's complexity and the growing interest in the field of wastewater surveillance. Our results show that transconductance offers far superior limits of detection but suffers from limited reproducibility, a narrower dynamic range, and is ineffective for some viral proteins. In comparison, we find that Dirac point tracking offers higher reproducibility and superior robustness, but at a higher limit of detection. Interestingly, both techniques exhibit similar sensitivity, highlighting the importance of the aptamers employed. Systematic experiments also help explain differences in dynamic range and limited functionality in detecting some proteins, resulting from unintentional electrophoresis, and shifting the high transconductance point away from the active region. Thus, our findings provide crucial considerations for designing and operating resilient graphene biosensors suitable for real-time pathogen monitoring in environmental scenarios. 

Abstract A deeper understanding of resistive switching (RS) in 2D materials is essential for advancing neuromorphic computing. The Dissociation-Diffusion-Adsorption (DDA) model offers a useful framework for probing RS mechanisms in non-volatile memory (NVM) and in-memory computing. We have employed first-principles density functional theory (DFT) to explore dissociation, diffusion, and adsorption phenomena within the DDA model, focusing on the interactions between exemplary metal atoms (Au, Ag, Cu) and monolayer MoS₂. Nudged elastic band (NEB) calculations evaluated diffusion barriers in pristine and sulfur-vacancy MoS₂. Charged systems were modeled to assess the impact of applied bias on migration pathways. We also examined metal dissociation from bulk electrodes and adsorption at S vacancies. Ag/MoS₂shows the lowest dissociation barrier (~0.034 eV), while Au and Cu exhibit similar values (~0.32 eV). These insights highlight Ag as a promising candidate for low-energy RS applications and provide guidance for optimizing switching efficiency in 2D memory devices. 

Abstract A synaptic memristor using 2D ferroelectric junctions is a promising candidate for future neuromorphic computing with ultra‐low power consumption, parallel computing, and adaptive scalable computing technologies. However, its utilization is restricted due to the limited operational voltage memory window and low on/off current (I_ON/OFF) ratio of the memristor devices. Here, it is demonstrated that synaptic operations of 2D In₂Se₃ferroelectric junctions in a planar memristor architecture can reach a voltage memory window as high as 16 V (±8 V) and I_ON/OFFratio of 10⁸, significantly higher than the current literature values. The power consumption is 10⁻⁵ W at the on state, demonstrating low power usage while maintaining a large I_ON/OFFratio of 10⁸compared to other ferroelectric devices. Moreover, the developed ferroelectric junction mimicked synaptic plasticity through pulses in the pre‐synapse. The nonlinearity factors are obtained 1.25 for LTP, −0.25 for LTD, respectively. The single‐layer perceptron (SLP) and convolutional neural network (CNN) on‐chip training results in an accuracy of up to 90%, compared to the 91% in an ideal synapse device. Furthermore, the incorporation of a 3 nm thick SiO₂interface between the α‐In₂Se₃and the Au electrode resulted in ultrahigh performance among other 2D ferroelectric junction devices to date. 

Analog neuromorphic computing systems emulate the parallelism and connectivity of the human brain, promising greater expressivity and energy efficiency compared to those of digital systems. Though many devices have emerged as candidates for artificial neurons and artificial synapses, there have been few device candidates for artificial dendrites. In this work, we report on biocompatible graphene-based artificial dendrites (GrADs) that can implement dendritic processing. By using a dual side-gate configuration, current applied through a Nafion membrane can be used to control device conductance across a trilayer graphene channel, showing spatiotemporal responses of leaky recurrent, alpha, and Gaussian dendritic potentials. The devices can be variably connected to enable higher-order neuronal responses, and we show through data-driven spiking neural network simulations that spiking activity is reduced by ≤15% without accuracy loss while low-frequency operation is stabilized. This positions the GrADs as strong candidates for energy efficient bio-interfaced spiking neural networks.