NANOCOM '25: Proceedings of the 12th Annual ACM International Conference on Nanoscale Computing and Communication

Concentration-shift keying (CSK) is the classical modulation for diffusion-based molecular communication, but implementations remain almost binary: on-off keying (OOK, two symbols) is standard and only a few reports reach more symbols. Higher-order CSK would raise efficiency, yet two hurdles persist—strong inter-symbol interference (ISI) and the limited molecule payload of a nanomachine. We present a feasible linear high-order concentration modulation (LHOCM) with Gaussian-Intersection threshold and limited molecule payload that overcomes both obstacles in a purely statistical manner. First, closed-form expressions for ISI mean and variance in a 3-D diffusion channel are derived. Using these, we upper-bound the BER with Gaussian Intersection thresholds that statistically compensates ISI, and obtain a simple sizing rule that links channel quality, molecule payload and constellation order. Numerical results show that, under a realistic budget of molecules per symbol, LHOCM attains a obvious gain over OOK using the same payload. The work thus supplies (i) the first reproducible high-order CSK baseline, and (ii) a one-line design tool for power-constellation sizing in nanonetworks. The proposed LHOCM is algorithm-agnostic and can be directly embedded into existing CSK-related modulation architectures.

Traditional single-input-single-output (SISO) models fall short in multi-receiver molecular communication due to mutual interference and complex spatial distribution. This study proposes a single-input-multiple-output (SIMO) 3D unbounded diffusion model with a point transmitter and two spherical receivers, incorporating finite surface receptors and reversible ligand-receptor reactions. A probabilistic equation for binding and the expected received signal (ERS) are derived. To handle spatial interference, a probabilistic equivalent modeling (PEM) approach and a multiple receiver iteration (MRI) algorithm are proposed. The MRI algorithm decomposes interference into direct and indirect components to iteratively compute each receiver's ERS. Simulations confirm the model's accuracy, and further analysis shows that optimizing receiver layout effectively reduces interference and improves system performance.

Molecular communication (MC) enables information exchange at the nano- and microscale, with applications in areas like drug delivery and health monitoring. These event-driven scenarios often require alternatives to traditional transmission. Identification communication, introduced by Ahlswede and Dueck, offers such an approach, in which the receiver only determines whether a specific message was sent, suiting resource-limited and event-triggered systems. This paper combines MC with identification and proposes a one-dimensional (1D) diffusion-based model. Diffusion noise is modeled as a Poisson process, and a lower bound on channel capacity is derived. Simulations, microscopic, and with short-length deterministic codes, validate theoretical results, including the channel impulse response and error bounds. The findings support the design of practical MC systems, with potential use in testbed development.

Air-based molecular communication (MC) has the potential to be one of the first MC systems to be deployed in real-world applications, enabled by existing sensor technologies such as metal-oxide semi-conductor (MOS) sensors. However, commercially available sensors usually exhibit non-linear and cross-reactive behavior, contrary to the idealizing assumptions about linear and perfectly molecule type-specific sensing often made in the MC literature. To address this gap, we propose a detector for molecule mixture communication with a general non-linear, cross-reactive receiver (RX) array that performs approximate maximum likelihood detection on the sensor outputs. Additionally, we introduce an algorithm for the design of mixture alphabets that accounts for the RX characteristics. We evaluate our detector and alphabet design algorithm through simulations that are based on measurements reported for two commercial MOS sensors. Our simulations demonstrate that the proposed detector achieves similar symbol error rates as data-driven methods without requiring large numbers of training samples and that the alphabet design algorithm outperforms methods that do not account for the RX characteristics. Since the proposed detector and alphabet design algorithm are also applicable to other chemical sensors, they pave the way for reliable air-based MC.

Molecular communication is a core pillar of the Internet of Bio-Nano Things. Exhaled breath, rich in water vapor, offers a viable medium for air-based molecular communication. This paper presents a low-cost, non-invasive approach using a DHT22 sensor to classify breath patterns, namely Eupnea, Bradypnea, and Tachypnea. Humidity and temperature signals from the mouth and nose are processed using machine learning (ML). The model achieves strong classification performance, showing that ML can effectively distinguish breath patterns despite sensor constraints.

Molecular Communications (MC) is an emerging research paradigm that utilizes molecules to transmit information, with promising applications in biomedicine such as targeted drug delivery or tumor detection. It is also envisioned as a key enabler of the Internet of BioNanoThings (IoBNT). In this paper, we propose algorithms based on Recurrent Neural Networks (RNN) for the estimation of communication channel parameters in MC systems. We focus on a simple branched topology, simulating the molecule movement with a macroscopic MC simulator. The Deep Learning architectures proposed for distance estimation demonstrate strong performance within these branched environments, highlighting their potential for future MC applications.

Flow-guided Localization (FGL) enables the identification of spatial regions within the human body that contain an event of diagnostic interest. FGL does that by leveraging the passive movement of energy-constrained nanodevices circulating through the bloodstream. Existing FGL solutions rely on graph models with fixed topologies or handcrafted features, which limit their adaptability to anatomical variability and hinder scalability. In this work, we explore the use of Set Transformer architectures to address these limitations. Our formulation treats nanodevices' circulation time reports as unordered sets, enabling permutation-invariant, variable-length input processing without relying on spatial priors. To improve robustness under data scarcity and class imbalance, we integrate synthetic data generation via deep generative models, including CGAN, WGAN, WGAN-GP, and CVAE. These models are trained to replicate realistic circulation time distributions conditioned on vascular region labels, and are used to augment the training data. Our results show that the Set Transformer achieves comparable classification accuracy compared to Graph Neural Networks (GNN) baselines, while simultaneously providing by-design improved generalization to anatomical variability. The findings highlight the potential of permutation-invariant models and synthetic augmentation for robust and scalable nanoscale localization.

Despite advances in medical research and therapy, cancer remains a major global health issue due to its complexity in treatment. Depending on the type and extent of the disease, tumor removal with cancer treatment drugs without harming surrounding healthy tissue is a critical challenge. The targeted drug delivery (TDD) approach has emerged to increase the effectiveness of treatment while reducing side effects. This paper envisions a hybrid navigation mechanism that utilizes chemotaxis and entropy to direct nanoscale medical agents (NMAs) toward cancerous cells within a tumor microenvironment (TME) to release medication and treat them. The NMAs sense the concentration gradient of the biomarkers in their vicinity to localize the tumor(s). For this study, we used hypoxia as our biomarker, given the higher oxygen consumption by cancerous cells. The numerical results show that the effectiveness and reliability of the hybrid navigation strategy are significantly higher than those of the random and chemotaxis navigation mechanisms. Furthermore, analysis of cancerous cell statistics over time demonstrates that the proposed method eliminates tumors faster than the other two strategies.

Galvanic Coupling (GC) Intra-Body Communication offers a promising solution for reliable, low-power data transmission in wearable medical devices. However, signal performance is highly sensitive to electrode material and skin-electrode interface properties. This study uses finite element modeling (FEM) to quantify the effects of electrode design, including material choice, conductive gel, and foam layers, on the GC channel frequency response (CFR), focusing on attenuation, phase delay, and group delay. Results show that incorporating gel and foam significantly enhances signal transmission by reducing attenuation, minimizing phase distortion, and stabilizing group delay across a broad frequency range. These improvements help mitigate performance disparities between Ag/AgCl and copper (Cu) electrodes, supporting the development of high-performance, energy-efficient intra-body networks for wearable healthcare applications.

The molecular communication (MC) research field has been moving towards more realistic modeling and concrete experimental setups. One of the most promising carriers for MC are DNA strands, as they contain large amounts of information and can be flexibly designed for different scenarios. In this work, we present an experimental MC system that uses DNA strand displacement to create microscale transmitter (TX) and receiver (RX) beads. As a physical framework, we focus on the microfluidic interaction channel (MIC) allowing for a central flow channel with parallel channels to either side separated by permeable membranes, roughly mimicking real scenarios, for example, in blood vessels with reactive sidewalls. We present an extensive numerical study of different mathematical modeling and simulation approaches for the MIC with DNA-based TX and RX beads, including partial differential equation (PDE) solvers, simplified analytical solutions, and convolution-based approximations. Our results show that the analytical and convolution models often provide accurate approximations and a good trade-off between detail and computational complexity. Lastly, we present initial results from the experimental setup, and give an outlook on the potential parameter fitting and optimization opportunities that arise from combining efficient models with a realistic practical testbed.

This paper proposes a distributed scheduling algorithm for time-division multiple access (TDMA) in molecular communication via diffusion. We consider a scenario with a receiver node surrounded by multiple transmitter nodes. The objective of the presented algorithm is to align the transmissions in time, preventing inter-user interference. It requires no synchronization and only needs the capability of listening to the channel and locally measuring time. Furthermore, only one molecule type is required. We compare the efficiencies of the proposed scheduling algorithm and an ideal, centralized scheduling algorithm and validate it through a particle-based simulation.

Miniaturized medical devices have become a research focus due to their advantages for use within the body. However, their reliance on conventional wireless or Bluetooth communication methods poses significant challenges in the in-body environment, including signal attenuation by biological tissues, susceptibility to electromagnetic interference, and high power consumption. These limitations hinder the clinical application and further development of such devices. To address these challenges, this study proposes a novel neuro-gastrointestinal (GI) axis bio-communication system based on the GI response to neural stimulation. In in vivo experiments on rabbits, electrical stimulation is applied to the left cervical vagus nerve, while GI motility rhythms are continuously recorded in real-time using a tonotransducer. The regulatory effects of different stimulation parameters on GI peristaltic rhythms were systematically analyzed, and the reliable transmission capability of the proposed communication system is validated through the delivery of binary encoded signals. This study provides a novel, tunable, and biocompatible communication method for in vivo medical devices, expanding the application potential of bio-communication technologies.

Molecular communication (MC) is an emerging paradigm for information exchange that utilizes molecular information carriers. DNA molecular communication (DNA-MC) offers high information density, programmability, and biocompatibility. Recent progress in track hopper-based DNA-MC introduced a directional motion mechanism, in contrast to traditional stochastic diffusion, where molecular hoppers actively transport DNA cargo on tracks. While this controlled transport improves precision and efficiency, it also gives rise to new challenges, including systematic errors such as inter-symbol interference and correlated backstepping, as well as mutation errors from DNA synthesis and sequencing processes, including insertions, deletions, and substitutions. This paper proposed Sentry-Weave Interleaved Code (SWIC), a hybrid channel coding scheme for track hopper-based DNA-MC systems. The scheme enhances biocompatibility by integrating run-length limited (RLL) coding constraints, achieves error correction using low-density parity-check (LDPC) codes, and mitigates burst interference caused by consecutive backstepping errors of molecular hoppers through interleaving coding, thereby simultaneously addressing both systematic and mutation errors while satisfying biocompatibility constraints. This work paves the way for high-throughput, low-error, and biocompatible molecular information exchange within living environments.

Molecular communication (MC) represents a novel paradigm that utilizes biochemical signals for information transfer, presenting a viable alternative for scenarios where conventional electromagnetic communication is not practical. Despite significant advancements in theoretical models, experimental realizations remain limited due to the fundamental complexity of biological systems. Although synthetic biology provides a tool to realize in vivo MC systems, its integration into the communication field is impeded by complex biological factors and the absence of readily available design methodologies. In this work, we present a structured workflow for developing in vivo MC systems using synthetic biology. Our approach guides researchers through genetic circuit design, enzyme-based DNA assembly, and validation techniques using quantifiable gene expression outputs. We demonstrate the feasibility of this workflow through a case study, where engineered Escherichia Coli (E-coli) bacteria detect acyl-homoserine lactones (AHL) signals and produce measurable fluorescence in response to varying concentrations. Our results confirm the reliability and responsiveness of the engineered receiver, validating our proposed design strategy. Importantly, this work facilitates broader adoption of in vivo MC systems and bridges the gap between theoretical modeling and practical implementation in biological environments.

Synthetic molecular communication (SMC) is a key enabler for future healthcare systems in which Internet of Bio-Nano-Things (IoBNT) devices facilitate the continuous monitoring of a patient's biochemical signals. To close the loop between sensing and actuation in these systems, both the detection and the generation of in-body molecular communication (MC) signals is key. However, generating signals inside the human body, e.g., via synthetic nanodevices, still poses a major research challenge in SMC, due to technological obstacles as well as legal, safety, and ethical issues. In contrast to many existing studies, this paper considers an SMC system in which signals are generated indirectly via the modulation of a natural in-body MC system, namely the gut-brain axis (GBA). Therapeutic GBA modulation is already established as treatment for some neurological diseases, e.g., drug refractory epilepsy (DRE), and performed via the administration of nutritional supplements or specific diets with therapeutic effect. However, the molecular signaling pathways that mediate the effect of such treatments are mostly unknown. Consequently, existing treatments are standardized or designed heuristically and able to help only some patients while failing to help others. In this paper, we propose to leverage personal health data, e.g., data gathered by in-body IoBNT devices, to overcome this research gap and design more versatile and robust GBA modulation-based treatments as compared to the existing ones. To show the feasibility of our approach, we first define a catalog of theoretical requirements for therapeutic GBA modulation. Then, we propose a machine learning model to verify these requirements for practical scenarios when only limited data on the GBA modulation is available. By evaluating the proposed model on several published datasets, we confirm its excellent accuracy in identifying different modulators of the GBA. Finally, we utilize the proposed model to identify specific modulatory pathways that play an important role for therapeutic GBA modulation. The results presented in this paper may help to develop novel personalized GBA-based treatments, i.e., novel nutritional supplements and/or diets, to help patients that do not respond to existing standardized treatments.

This paper investigates the localization of an unintended fully absorbing receiver in an unbounded three-dimensional molecular communication via diffusion (MCvD) system. The system consists of a point source transmitter, a known fully absorbing target receiver, and an unintended fully absorbing receiver. Unlike conventional single-receiver scenarios, the presence of two fully absorbing receivers induces molecular absorption competition, altering the expected received signal distribution and increasing the complexity of localization. To address this challenge, we employ maximum likelihood estimation (MLE) and the Newton-Raphson iterative method to estimate the distances between the transmitter and the unintended receiver, as well as between the target receiver and the unintended receiver. The likelihood function is formulated based on the number of molecules absorbed by the target receiver under the influence of the unintended receiver, and the receiver position is estimated iteratively through optimization. Simulation results validate the effectiveness of the proposed method, demonstrating its capability to accurately localize the unintended fully absorbing receiver despite interference from two fully absorbing receivers.

Molecular matrix computing devices play a major role in translating concepts from classical engineering into the bio-nano-domain and form the basis for artificial biological Neural Networks (NNs). Different concepts for the realization of such devices have been proposed in the literature. There exists a trade-off between computing speed and complexity of the realized matrix/NN, which dictates the appropriate choice for different application scenarios. In this work, we focus on computing speed for comparatively simple computations, i.e., matrix multiplications. We show that a previously proposed reaction-diffusion based computing structure, called M3N, will likely be faster than originally anticipated, given that the involved chemicals are chosen appropriately. The reason is that in previous theoretical studies, an idealized reaction model was utilized. Incorporating a thermodynamically more accurate model, we find that the computational speed in a real system can be higher due to previously neglected non-idealities. This finding is explained theoretically and supported by computer simulations.

In agriculture, molecular communication (MC) is envisioned as a framework to address critical challenges such as smart pest control. While conventional approaches mostly rely on synthetic plant protection products, posing high risks for the environment, harnessing plant signaling processes can lead to innovative approaches for nature-inspired sustainable pest control. In this paper, we investigate an approach for sustainable pest control and reveal how the MC paradigm can be employed for analysis and optimization. In particular, we consider a system where herbivore-induced plant volatiles (HIPVs), specifically methyl salicylate (MeSA), is encapsulated into microspheres deployed on deployed on plant leaves. The controlled release of MeSA from the microspheres, acting as transmitters (TXs), supports pest deterrence and antagonist attraction, providing an eco-friendly alternative to synthetic plant protection products. Based on experimental data, we investigate the MeSA release kinetics and obtain an analytical model. To describe the propagation of MeSA in farming environments, we employ a three dimensional (3D) advection-diffusion model, incorporating realistic wind fields which are predominantly affecting particle propagation, and solve it by a finite difference method (FDM). The proposed model is used to investigate the MeSA distribution for different TX arrangements, representing different practical microsphere deployment strategies. Moreover, we introduce the coverage effectiveness index (CEI) as a novel metric to quantify the environmental coverage of MeSA. This analysis offers valuable guidance for the practical development of microspheres and their deployment aimed at enhancing coverage and, consequently, the attraction of antagonistic insects.

Cancer remains a leading global health challenge, with early detection being crucial to improving patient outcomes. The tumor microenvironment (TME), characterized by abnormal oxygen and pH distributions, plays a vital role in tumor progression and treatment response. Recent advances in nanotechnology have enabled the development of micro/nanorobots capable of real-time sensing and targeted delivery within the TME. However, the effectiveness of such systems depends on the accurate modeling of biological gradient fields (BGFs). In this study, we propose a simulation framework grounded in Computational Nanobiosensing (CONA) to integrate vascular morphology with oxygen gradient field analysis for a more comprehensive understanding of the TME. Using COMSOL Multiphysics, we constructed a two-dimensional model that couples laminar flow and mass transport to investigate oxygen distribution under conditions in the presence and absence of tumors. The simulation results reveal that tumors significantly alter local oxygen profiles, leading to steep concentration gradients and hypoxic regions. These findings provide both visual and quantitative insight into the TME and establish a theoretical foundation for designing oxygen gradient-guided nanoparticle delivery strategies. This work contributes to the advancement of intelligent navigation and precision therapy in complex tumor environments.

This paper presents a novel physics-based model for signal propagation in closed-loop molecular communication (MC) systems, which are particularly relevant for many envisioned biomedical applications, such as health monitoring or drug delivery within the closed-loop human cardiovascular system (CVS). Compared to open-loop systems, which are mostly considered in MC, closed-loop systems exhibit different characteristic effects influencing signaling molecule (SM) propagation. One key phenomenon are the periodic SM arrivals at the receiver (RX), leading to various types of inter-symbol interference (ISI) inherent to closed-loop system. To capture these characteristic effects, we propose an analytical model for the SM propagation inside closed-loop systems. The model accounts for arbitrary spatio-temporal SM release patterns at the transmitter (TX), and incorporates several environmental effects such as fluid flow, SM diffusion, and SM degradation. Moreover, to capture a wide range of practically relevant degradation and clearance mechanisms, the model includes both local removal (e.g., due to SM absorption into organs) and global removal (e.g., due to chemical degradation) of SMs. The accuracy of the proposed model is validated with three-dimensional (3-D) particle-based simulations (PBSs). Moreover, we utilize the proposed model to develop a rigorous characterization of the various types of ISI encountered in closed-loop MC systems.

In molecular communication, inter symbol interference (ISI) severely degrades system performance. This paper proposes a channel coding optimization scheme for the Absorption Shift Keying (AbSK), in molecular communication system to enhance performance and reduce the bit error rate (BER). A dynamic codebook search mechanism is designed using the genetic algorithm (GA), which globally optimizes the codebook to adapt to the unique channel characteristics of AbSK. The design of the codebook optimized by genetic algorithm provides an effective coding idea for the AbSK system and improves the performance of the system.

Unlike most existing modulation schemes for molecular communications that convey information by the properties of message molecules, molecular absorption shift keying is a recently proposed scheme that leverages channel states to convey information additionally while providing energy for molecule harvesting nodes. However, the principles of molecular absorption shift keying are customized for the scenario with only one molecule harvesting node, which restricts its versatility. In this paper, we propose a new modulation technique, termed molecular type permutation absorption modulation (MTPAM), where the transmitter encodes the information into the permutation of molecular types of the message molecules and the passive/absorbing states of molecule harvesting nodes. These harvesting nodes can change their absorption state, thereby altering the channel states. We designed both an ideal maximum likelihood detector and a two-step detector for MTPAM systems. The upper bound of the bit error rate (BER) is derived for both detectors. Simulation results show that our scheme outperforms the existing modulation schemes in terms of BER.

Molecular communication (MC) transmits information through the release, diffusion, and reception of molecules, holding great potential in the field of drug delivery. In an MC system, the prediction of the distance between the transmitter and the receiver is crucial for the receiver's resource consumption. Traditional distance detection strategies mainly focus on known channel state information (CSI). To address this limitation, this paper proposes a method for estimating the distance between the transmitters and the receiver in MC system with unknown CSI using a deep neural network (DNN) model. We employ Monte Carlo simulation to capture the positions of molecules in a three-dimensional environment. The dataset is generated based on the molecular coordinates at each position. Numerical results indicate that the DNN model can accurately estimate the distance between the transmitters and the receiver, demonstrating good detection capabilities and generalization ability. Additionally, the minimum distance between the transmitters and the receiver's boundary also affects the accuracy of the distance estimation.

In molecular communication (MC) systems, energy consumption plays a critical role in determining the bit error rate (BER) performance. This paper investigates an MC system with an imperfect transmitter that collects two types of molecules from the environment and releases them as a mixture. The receiver is equipped with receptors that selectively bind only to one target molecule type. The presence of non-target (interference) molecules in the transmitted mixture weakens the effective signal strength, thereby degrading detection accuracy. To mitigate this issue, the transmitter can selectively remove non-target molecules, enhancing the molecular purity of the transmitted signal. However, this purification process incurs additional energy consumption, introducing a fundamental trade-off between energy efficiency and communication reliability. To address this, we formulate a tradeoff function that jointly characterizes the energy consumption and BER performance. A grid search algorithm is then employed to identify the optimal energy allocation that minimizes the tradeoff function. Theoretical analysis and simulation results confirm the validity of the proposed framework, highlighting its utility in optimizing energy-efficient design for MC systems operating under energy constraints.

Molecular Communication (MC), as an emerging paradigm in nanoscale communication, has been widely adopted for modeling and optimizing information transfer in biological systems. Calcium ions (Ca²⁺), due to their critical role in intracellular signal regulation, have become a focal point of research in this field. In this paper, we propose a transmission enhancement mechanism for MC using Ca²⁺ signal that leverages cellular chemotaxis and relay placement mechanisms. An improved cell migration model is first developed to guide cell movements toward target regions, mitigating the effects of random diffusion. Then, the signal propagation process is analyzed using stochastic geometry tool. A performance metric is formulated to optimize relay placement under constraints of energy and relay location. Simulation results demonstrate that the proposed model activates a greater number of cells in the target region and achieves a longer signal transmission range, significantly enhancing the overall communication performance of the system.

Molecular communication (MC) enables information exchange via chemical signals, offering distinct advantages in environments where electromagnetic communication is ineffective. In this work, we present the first experimental demonstration of an MC system with a function of integrated sensing and communication (ISAC). Specifically, alcohol molecules are modulated using on-off keying and detected by a chemical sensor that enables both binary decoding and real-time odor source localization. A mobile robotic platform interprets molecular concentration variations to navigate and recover messages. The results validate the feasibility of this dual function system, paving the way for future MC with embedded sensing capabilities.

Standard practices in microbiology often require genetically modifying the operation of cells. The process is particularly complex and time-consuming as it involves manipulating cells at the gene level. To overcome these challenges, we introduce a conceptual design for a non-contact and non-invasive procedure that utilizes terahertz waveforms. We propose a method to suppress gene expression by targeting the enzyme associated with the gene. Building on techniques from the reported literature, we outline a procedure in which terahertz waveforms transfer energy to a targeted enzyme within yeast cells. Upon energy transfer, the enzyme unfolds, which prevents it from catalyzing the associated metabolic reactions. As a result, this makes the cell deficient in a particular gene expression on demand.

The Cellular Potts Model (CPM) is a lattice-based computational framework for simulating tissue dynamics such as cell sorting, collective cell migration, and morphogenesis. While the model effectively captures essential biological properties, its high computational cost poses a significant challenge when simulating large-scale multicellular systems over extended simulation times. In our previous work, we proposed a boundary-based algorithm that reduces computation time by limiting copy attempts to cell boundaries and performing local energy calculations. In this paper, we further accelerate CPM simulations with a CPU-GPU hybrid boundary-based algorithm: local energy calculations are offloaded to the GPU to leverage its parallel processing capabilities, while the CPU continues to manage cell boundaries and connectivity checks. Benchmark results demonstrate a substantial reduction in computation time compared to the CPU-based boundary-based algorithm, enabling more efficient and scalable simulations of complex multicellular systems.

Artificial Intelligence (AI) tools such as ChatGPT have transformed our daily life. Usually, these AI tools are based on Neural Networks (NNs) implemented in digital hardware, i.e., not biological or physical NNs like those found in human brains. Instead, NNs are typically implemented as software on top of a Graphics Processing Unit (GPU) or specialized AI hardware. In contrast, this work-in-progress utilizes a biological NN, demonstrating the use of Synthetic Biological Intelligence (SBI) by implementing a toy example - controlling an inverted pendulum. Biological NNs can be beneficial compared to GPUs or specialized AI hardware, especially in terms of energy efficiency and capability on noisy or dynamic tasks. Following the encoding, decoding, reward, and punishment nature of the biological NN, this work presents initial results toward controlling the physical system in future research.

This paper introduces an experimental platform for airborne molecular communication (AMC) and investigates the feasibility of using metal oxide semiconductor (MOS) sensors as receivers. The platform enables chemical communication with volatile organic compounds (VOCs), specifically ethanol and acetone, under repeated dosing patterns with controlled airflow. Preliminary experiments show that ethanol and acetone yield different sensor responses, indicating the potential of MOS sensors to facilitate the decoding of molecular signals. Distinguishable temporal response patterns are also shown, highlighting the possibilities and challenges of selective chemical detection in AMC. Future work will extend the study to molecular mixture signals.

The inherent randomness of diffusion leads to limited signal strength and pronounced inter-symbol interference (ISI) in molecular communications (MC), posing significant challenges to its further applications. This study proposes a MC system where a reflector is equipped at the transmitter to reflect the information molecules and eventually increase the number of the expected received molecules. An asymmetric single-input multi-output (SIMO) channel model is presented to approximate the single-input single-output (SISO) reflector-assisted MC system, of which the model coefficients are estimated using a training-based approach. The numerical results show that the reflector-assisted MC system can effectively enhance the received signal strength and mitigate the tailing effect.

Advancing Molecular Communications (MC) toward real-world applications remains a critical challenge in bio-nanotechnology. In this work, a recently developed yeast-based MC testbed is leveraged to engineer a novel yeast-derived biosensor, designed to function as an MC receiver within the Internet of Bio-Nano Things (IoBNT) paradigm, offering a biologically integrated solution for real-time molecular signal detection. The biosensor converts gut copper toxicity into an optical output, enabling integration with external systems and potential Smart Pill deployment for continuous monitoring. Design principles are outlined, and a preliminary model of a unique signaling pathway is developed, paving the way for experimental validation and signal detection mechanisms.

In environments where airborne molecular communication (AMC) is used, the choice of surface materials can significantly affect system performance by altering the concentration and persistence of molecular signals. To gain first insights into these material-dependent effects, we performed experiments in a small closed chamber with a high surface-to-volume ratio and compared the observed AMC signals when varying the inner surface between borosilicate glass and polyethylene (PE)-based plastic. In contrast to plastic, the glass environment produced a signal with a more distinct pattern and shorter duration, showing the impact of material in the environment on molecular retention. This behavior can be attributed to differences in surface interactions, in particular the adsorption (and potentially partial absorption) and subsequent desorption of molecules on plastic surfaces. Our results underline the need to explicitly consider the properties of surface materials in the development and modeling of AMC systems.

In molecular communication (MC), information is encoded in the properties of molecules stored at the transmitter. Therefore, designing an energy-efficient transmitter is essential for effective MC system performance. This paper considers a transmitter model composed of two reservoirs, where information molecules are collected from the environment and stored. To establish a concentration difference for information encoding, we explore two molecular movement strategies: (i) transferring molecules between the reservoirs and (ii) transferring molecules from the reservoirs to the external environment. We analyze the transmitter's performance under both strategies, considering energy consumption and the initial molecular state. Comparative results show that transferring molecules between reservoirs yields better Bit Error Rate (BER) performance, as these molecules contribute more effectively to signal formation. Overall, this study offers key insights into designing energy-efficient transmitters for molecular communication systems.

This paper proposes a method for accurately estimating the relative position between two nodes with unknown locations in a diffusion-based molecular communication environment. A specialized node structure is designed, combining a central absorbing receiver with multiple transmitters placed at predefined spherical coordinates. Pilot molecules are released, and their absorption time and concentration are measured. By partitioning the spherical coordinate space, these spatially distinct measurements serve as input to a multilayer perceptron (MLP)-based model. The proposed method significantly improves the precision of distance and direction estimation. Simulation results demonstrate localization accuracy, confirming the effectiveness of the neural network model in capturing the underlying physical characteristics.

Personalized medicine increasingly relies on advanced simulations to support treatment planning. MEHLISSA is a simulation tool for in-body communication and disease modeling in the human circulatory system. Its previous implementation, based on the ns-3 framework, was computationally intensive and not suitable for large-scale biological simulations. In this work, we present MEHLISSA 2.0, a redesigned version with a streamlined simulation core. We demonstrate its applicability by simulating a typical large-scale molecular communication environment: CAR-T cell leukemia therapy based on established biological models. Benchmarking shows substantial performance improvements, particularly in long simulations with over 2x runtime reduction, which marks a significant step toward simulating realistic treatment scenarios.

Airborne transmission is the main route for respiratory virus droplet dispersion, making quantitative analysis of droplet diffusion dynamics crucial for public health. In this study, a molecular communication platform was constructed. The platform employs an acrylic chamber to simulate indoor environments, uses an electric spray device to mimic various respiratory behaviors of infectious sources (such as coughing), and incorporates fans to regulate airflow.

Neuron-microglia communication is essential for brain function but remains poorly quantified. We present a combined experimental and computational framework using the NAOM TetraCulture system and a molecular communication model with information-theoretic analysis. Our model captures ATP and cytokine signaling dynamics and quantifies information flow between neurons and microglia under normal and inflammatory conditions. This approach advances understanding of neuroimmune interactions and aids the identification of therapeutic strategies.

Molecular communication's diffusion characteristics limit data transmission rates. While Multiple-Input Multiple-Output (MIMO) technology increases rates, it also intensifies inter-symbol interference (ISI) and inter-link interference (ILI), complicating channel modeling. Traditional neural network detectors are computationally intensive, failing to meet low complexity and energy requirements. This paper proposes a Mamba neural network-based detector, which reduces complexity and improves performance, making it more suitable for molecular communication systems compared to Transformer.

For testbed experiments in molecular communication, the injection method can significantly impact the results of the received signals. In this work, the impact of the injection flow rate on the uniform distribution of particles was studied by numerical simulation of a syringe injection in the direction of background flow for three different flow rates in 3D. A low injection rate seems to negatively impact the overall propagation of the particle bolus.

The current work in molecular communication employs populations of vesicles as biological senders and receivers. Sender vesicles convert glucose oxidase (GOx) or lactate oxidase (LOx) into hydrogen peroxide (H₂O₂), which then functions as the communication medium. Receiver vesicles, incorporating horseradish peroxidase (HRP), recognize and process H₂O₂ into a fluorescent output. We establish models that cover both the kinetics of enzymatic reactions and spatial diffusion with degradation to signal intensity and effective range. The results from simulations indicate that both sender-to-receiver proportions and physical spacing impact the signal's vigor and reliability. Experimental validation using microfluidic platforms and fluorescent measurements is planned for future investigations.

Superparamagnetic iron oxide nanoparticles (SPIONs) often form chain-like agglomerates due to their magnetic dipole interactions. Various simulation methods have been employed to elucidate the underlying physical mechanisms; however, experimental validation using realistic ferrofluid parameters remains limited. In this study, we perform molecular dynamics simulations based on the Lennard-Jones potential to investigate the agglomerate size distribution using experimentally derived physical parameters.