A network of nanomachines (NMs) can be used to build a target detection system for a variety of promising applications. They have the potential to detect toxic chemicals, infectious bacteria, and biomarkers of dangerous diseases such as cancer within the human body. Many diseases and health disorders can be detected early and efficiently treated in the future by utilizing these systems. To fully grasp the potential of these systems, mathematical analysis is required. This paper describes an analytical framework for modeling and analyzing the performance of target detection systems composed of multiple mobile nanomachines of varying sizes with passive/absorbing boundaries. We consider both direct contact detection, in which NMs must physically contact the target to detect it, and indirect sensing, in which NMs must detect the marker molecules emitted by the target. The detection performance of such systems is calculated for degradable and non-degradable targets, as well as mobile and stationary targets. The derived expressions provide various insights, such as the effect of NM density and target degradation on detection probability.

翻译：纳米机器网络可构建用于多种有前景应用的目标检测系统。这类网络具有检测人体内有毒化学物质、传染性细菌及癌症等危险疾病生物标志物的潜力。通过利用这些系统，未来许多疾病和健康失调可实现早期发现并得到有效治疗。为充分理解此类系统的潜力，需要进行数学分析。本文提出了一种分析框架，用于建模和分析由不同尺寸、具有被动/吸收边界的多个移动纳米机器组成的目标检测系统的性能。我们同时考虑了两种检测模式：直接接触检测（纳米机器必须通过物理接触目标进行检测）和间接传感检测（纳米机器需检测目标释放的标记分子）。针对可降解与不可降解目标，以及移动与静止目标，计算了此类系统的检测性能。所推导的表达式揭示了多种关键特性，例如纳米机器密度及目标降解程度对检测概率的影响。