This article explores the operation of organic artificial neurons (OANs) in liquid environments, which are crucial for neuromorphic bioelectronics. The study combines experiments, numerical simulations, and analytical tools to understand the biorealistic behaviors of OANs, including firing properties, excitability, wetware operation, and biohybrid integration. Non-linear simulations are based on a physics-based framework that considers ion type, concentration, organic mixed ionic-electronic parameters, and biomembrane features. The derived analytical expressions link OAN spiking features with material and physical parameters, bridging the gap between artificial neurons and neuroscience. This work provides guidelines for the design, development, engineering, and optimization of OANs, advancing next-generation neuronal networks, neuromorphic electronics, and bioelectronics. The study also investigates the impact of various parameters on OAN performance, such as threshold voltage, transconductance, volumetric capacitance, and resistance, and demonstrates the ability to modulate spiking frequency and energy per spike. Additionally, the article explores the excitability and noise-induced activity of OANs, showing their ability to emulate biological neurons and robustly sense ion concentrations in a liquid environment. The findings highlight the potential of OANs for neuromorphic ion sensing and biointerfacing applications.This article explores the operation of organic artificial neurons (OANs) in liquid environments, which are crucial for neuromorphic bioelectronics. The study combines experiments, numerical simulations, and analytical tools to understand the biorealistic behaviors of OANs, including firing properties, excitability, wetware operation, and biohybrid integration. Non-linear simulations are based on a physics-based framework that considers ion type, concentration, organic mixed ionic-electronic parameters, and biomembrane features. The derived analytical expressions link OAN spiking features with material and physical parameters, bridging the gap between artificial neurons and neuroscience. This work provides guidelines for the design, development, engineering, and optimization of OANs, advancing next-generation neuronal networks, neuromorphic electronics, and bioelectronics. The study also investigates the impact of various parameters on OAN performance, such as threshold voltage, transconductance, volumetric capacitance, and resistance, and demonstrates the ability to modulate spiking frequency and energy per spike. Additionally, the article explores the excitability and noise-induced activity of OANs, showing their ability to emulate biological neurons and robustly sense ion concentrations in a liquid environment. The findings highlight the potential of OANs for neuromorphic ion sensing and biointerfacing applications.