Pub Date : 2024-07-02DOI: 10.1109/TNB.2024.3422280
Hadeel Elayan;Samar Elmaadawy;Andrew W. Eckford;Raviraj Adve;Josep Jornet
Proteins can be regarded as thermal nanosensors in an intra-body network. Upon being stimulated by Terahertz (THz) frequencies that match their vibrational modes, protein molecules experience resonant absorption and dissipate their energy as heat, undergoing a thermal process. This paper aims to analyze the effect of THz signaling on the protein heat dissipation mechanism. We therefore deploy a mathematical framework based on the heat diffusion model to characterize how proteins absorb THz-electromagnetic (EM) energy from the stimulating EM fields and subsequently release this energy as heat to their immediate surroundings. We also conduct a parametric study to explain the impact of the signal power, pulse duration, and inter-particle distance on the protein thermal analysis. In addition, we demonstrate the relationship between the change in temperature and the opening probability of thermally-gated ion channels. Our results indicate that a controlled temperature change can be achieved in an intra-body environment by exciting protein particles at their resonant frequencies. We further verify our results numerically using COMSOL Multiphysics® and introduce an experimental framework that assesses the effects of THz radiation on protein particles. We conclude that under controlled heating, protein molecules can serve as hotspots that impact thermally-gated ion channels. Through the presented work, we infer that the heating process can be engineered on different time and length scales by controlling the THz-EM signal input.
{"title":"A Thermal Study of Terahertz Induced Protein Interactions","authors":"Hadeel Elayan;Samar Elmaadawy;Andrew W. Eckford;Raviraj Adve;Josep Jornet","doi":"10.1109/TNB.2024.3422280","DOIUrl":"10.1109/TNB.2024.3422280","url":null,"abstract":"Proteins can be regarded as thermal nanosensors in an intra-body network. Upon being stimulated by Terahertz (THz) frequencies that match their vibrational modes, protein molecules experience resonant absorption and dissipate their energy as heat, undergoing a thermal process. This paper aims to analyze the effect of THz signaling on the protein heat dissipation mechanism. We therefore deploy a mathematical framework based on the heat diffusion model to characterize how proteins absorb THz-electromagnetic (EM) energy from the stimulating EM fields and subsequently release this energy as heat to their immediate surroundings. We also conduct a parametric study to explain the impact of the signal power, pulse duration, and inter-particle distance on the protein thermal analysis. In addition, we demonstrate the relationship between the change in temperature and the opening probability of thermally-gated ion channels. Our results indicate that a controlled temperature change can be achieved in an intra-body environment by exciting protein particles at their resonant frequencies. We further verify our results numerically using COMSOL Multiphysics® and introduce an experimental framework that assesses the effects of THz radiation on protein particles. We conclude that under controlled heating, protein molecules can serve as hotspots that impact thermally-gated ion channels. Through the presented work, we infer that the heating process can be engineered on different time and length scales by controlling the THz-EM signal input.","PeriodicalId":13264,"journal":{"name":"IEEE Transactions on NanoBioscience","volume":"24 1","pages":"78-88"},"PeriodicalIF":3.7,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141491740","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-01DOI: 10.1109/TNB.2024.3415195
{"title":"IEEE Transactions on NanoBioscience Information for Authors","authors":"","doi":"10.1109/TNB.2024.3415195","DOIUrl":"https://doi.org/10.1109/TNB.2024.3415195","url":null,"abstract":"","PeriodicalId":13264,"journal":{"name":"IEEE Transactions on NanoBioscience","volume":"23 3","pages":"C3-C3"},"PeriodicalIF":3.7,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10579905","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141495045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Human sperm functioning is crucial for maintaining natural reproduction, but its sterility is enhanced by variations in environmental conditions. Because of these agitating properties, powerful computer-aided devices are required, but their precision is inadequate, particularly when it comes to samples with low sperm concentrations. Therefore, for the first time, this article introduces the sulfide material-based structure for the detection of human sperm samples using the prism-based surface plasmon resonance sensor (SPR) Nano-biosensor. The proposed structure is designed on the basis of a prism-based Kretschmann configuration and includes silver, silicon, a sulfide layer, black phosphorus, and a sensing medium. This work takes advantage of the excitement of surface plasmons and evanescent waves in the metal dielectric region. For the detection process, seven sperm samples are taken, with their concentration, mobility, and refractive index measured by the refractometer. The proposed structure provides a maximum sensitivity of $409.17{^{circ }} mathord {left /{{vphantom {{^{circ }} {text {RIU}}}}}right . hspace {-1.2pt} } {text {RIU}}$ , QF of $97.45{text {RIU}}^{-{1}}$ and a DA of 1.37. The results provide a substantial improvement in comparison to the reported work in the literature.
{"title":"Design and Probing of Prism-Based SPR Nano-Biosensor for Human Sperm Detection","authors":"Yesudasu Vasimalla;Baljinder Kaur;Suman Maloji;Santosh Kumar","doi":"10.1109/TNB.2024.3419571","DOIUrl":"10.1109/TNB.2024.3419571","url":null,"abstract":"Human sperm functioning is crucial for maintaining natural reproduction, but its sterility is enhanced by variations in environmental conditions. Because of these agitating properties, powerful computer-aided devices are required, but their precision is inadequate, particularly when it comes to samples with low sperm concentrations. Therefore, for the first time, this article introduces the sulfide material-based structure for the detection of human sperm samples using the prism-based surface plasmon resonance sensor (SPR) Nano-biosensor. The proposed structure is designed on the basis of a prism-based Kretschmann configuration and includes silver, silicon, a sulfide layer, black phosphorus, and a sensing medium. This work takes advantage of the excitement of surface plasmons and evanescent waves in the metal dielectric region. For the detection process, seven sperm samples are taken, with their concentration, mobility, and refractive index measured by the refractometer. The proposed structure provides a maximum sensitivity of \u0000<inline-formula> <tex-math>$409.17{^{circ }} mathord {left /{{vphantom {{^{circ }} {text {RIU}}}}}right . hspace {-1.2pt} } {text {RIU}}$ </tex-math></inline-formula>\u0000, QF of \u0000<inline-formula> <tex-math>$97.45{text {RIU}}^{-{1}}$ </tex-math></inline-formula>\u0000 and a DA of 1.37. The results provide a substantial improvement in comparison to the reported work in the literature.","PeriodicalId":13264,"journal":{"name":"IEEE Transactions on NanoBioscience","volume":"24 1","pages":"70-77"},"PeriodicalIF":3.7,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141456449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-17DOI: 10.1109/TNB.2024.3415365
Yidan Zhang;Junchao Wang;Jinkai Chen;Guodong Su;Wen-Sheng Zhao;Jun Liu
The separation of biological particles like cells and macromolecules from liquid samples is vital in clinical medicine, supporting liquid biopsies and diagnostics. Deterministic Lateral Displacement (DLD) is prominent for sorting particles in microfluidics by size. However, the design, fabrication, and testing of DLDs are complex and time-consuming. Researchers typically rely on finite element analysis to predict particle trajectories, which are crucial in evaluating the performance of DLD. Traditional particle trajectory predictions through finite element analysis often inaccurately reflect experimental results due to manufacturing and experimental variabilities. To address this issue, we introduced a machine learning-enhanced approach, combining past experimental data and advanced modeling techniques. Our method, using a dataset of 132 experiments from 40 DLD chips and integrating finite element simulation with a microfluidic-optimized particle simulation algorithm (MOPSA) and a Random Forest model, improves trajectory prediction and critical size determination without physical tests. This enhanced accuracy in simulation across various DLD chips speeds up development. Our model, validated against three DLD chip designs, showed a high correlation between predicted and experimental particle trajectories, streamlining chip development for clinical applications.
{"title":"Machine Learning-Enhanced Predictive Modeling for Arbitrary Deterministic Lateral Displacement Design and Test","authors":"Yidan Zhang;Junchao Wang;Jinkai Chen;Guodong Su;Wen-Sheng Zhao;Jun Liu","doi":"10.1109/TNB.2024.3415365","DOIUrl":"10.1109/TNB.2024.3415365","url":null,"abstract":"The separation of biological particles like cells and macromolecules from liquid samples is vital in clinical medicine, supporting liquid biopsies and diagnostics. Deterministic Lateral Displacement (DLD) is prominent for sorting particles in microfluidics by size. However, the design, fabrication, and testing of DLDs are complex and time-consuming. Researchers typically rely on finite element analysis to predict particle trajectories, which are crucial in evaluating the performance of DLD. Traditional particle trajectory predictions through finite element analysis often inaccurately reflect experimental results due to manufacturing and experimental variabilities. To address this issue, we introduced a machine learning-enhanced approach, combining past experimental data and advanced modeling techniques. Our method, using a dataset of 132 experiments from 40 DLD chips and integrating finite element simulation with a microfluidic-optimized particle simulation algorithm (MOPSA) and a Random Forest model, improves trajectory prediction and critical size determination without physical tests. This enhanced accuracy in simulation across various DLD chips speeds up development. Our model, validated against three DLD chip designs, showed a high correlation between predicted and experimental particle trajectories, streamlining chip development for clinical applications.","PeriodicalId":13264,"journal":{"name":"IEEE Transactions on NanoBioscience","volume":"24 1","pages":"46-62"},"PeriodicalIF":3.7,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141418735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-30DOI: 10.1109/TNB.2024.3395420
Weng-Long Chang;Renata Wong;Yu-Hao Chen;Wen-Yu Chung;Ju-Chin Chen;Athanasios V. Vasilakos
Given an undirected, unweighted graph with n vertices and m edges, the maximum cut problem is to find a partition of the n vertices into disjoint subsets ${V}_{{1}}$ and ${V}_{{2}}$ such that the number of edges between them is as large as possible. Classically, it is an NP-complete problem, which has potential applications ranging from circuit layout design, statistical physics, computer vision, machine learning and network science to clustering. In this paper, we propose a biomolecular and a quantum algorithm to solve the maximum cut problem for any graph G. The quantum algorithm is inspired by the biomolecular algorithm and has a quadratic speedup over its classical counterparts, where the temporal and spatial complexities are reduced to, respectively, ${O}text {(}sqrt {{2}^{n}/{r}}text {)}$ and ${O}text {(}{m}^{{2}}text {)}$ . With respect to oracle-related quantum algorithms for NP-complete problems, we identify our algorithm as optimal. Furthermore, to justify the feasibility of the proposed algorithm, we successfully solve a typical maximum cut problem for a graph with three vertices and two edges by carrying out experiments on IBM’s quantum simulator.
给定一个有 n 个顶点和 m 条边的无向、无权重图，最大剪切问题就是将 n 个顶点划分为不相交的子集 ${V}_{{1}}$ 和 ${V}_{{2}}$ ，使它们之间的边的数量尽可能多。从经典上讲，这是一个 NP-完全问题，其潜在应用范围包括电路布局设计、统计物理、计算机视觉、机器学习和网络科学以及聚类。本文提出了一种生物分子算法和一种量子算法来解决任意图 G 的最大切割问题。量子算法受到生物分子算法的启发，与经典算法相比速度提高了四倍，其中时间复杂度和空间复杂度分别降低为 ${O}text {(}sqrt {{2}^{n}/{r}}text {)}$ 和 ${O}text {(}{m}^{2}}text {)}$ 。相对于 NP-complete问题的甲骨文相关量子算法，我们认为我们的算法是最优的。此外，为了证明所提算法的可行性，我们在 IBM 的量子模拟器上进行了实验，成功地解决了一个有三个顶点和两条边的图的典型最大切割问题。
{"title":"Bioinspired Quantum Oracle Circuits for Biomolecular Solutions of the Maximum Cut Problem","authors":"Weng-Long Chang;Renata Wong;Yu-Hao Chen;Wen-Yu Chung;Ju-Chin Chen;Athanasios V. Vasilakos","doi":"10.1109/TNB.2024.3395420","DOIUrl":"10.1109/TNB.2024.3395420","url":null,"abstract":"Given an undirected, unweighted graph with n vertices and m edges, the maximum cut problem is to find a partition of the n vertices into disjoint subsets \u0000<inline-formula> <tex-math>${V}_{{1}}$ </tex-math></inline-formula>\u0000 and \u0000<inline-formula> <tex-math>${V}_{{2}}$ </tex-math></inline-formula>\u0000 such that the number of edges between them is as large as possible. Classically, it is an NP-complete problem, which has potential applications ranging from circuit layout design, statistical physics, computer vision, machine learning and network science to clustering. In this paper, we propose a biomolecular and a quantum algorithm to solve the maximum cut problem for any graph G. The quantum algorithm is inspired by the biomolecular algorithm and has a quadratic speedup over its classical counterparts, where the temporal and spatial complexities are reduced to, respectively, \u0000<inline-formula> <tex-math>${O}text {(}sqrt {{2}^{n}/{r}}text {)}$ </tex-math></inline-formula>\u0000 and \u0000<inline-formula> <tex-math>${O}text {(}{m}^{{2}}text {)}$ </tex-math></inline-formula>\u0000. With respect to oracle-related quantum algorithms for NP-complete problems, we identify our algorithm as optimal. Furthermore, to justify the feasibility of the proposed algorithm, we successfully solve a typical maximum cut problem for a graph with three vertices and two edges by carrying out experiments on IBM’s quantum simulator.","PeriodicalId":13264,"journal":{"name":"IEEE Transactions on NanoBioscience","volume":"23 3","pages":"499-506"},"PeriodicalIF":3.7,"publicationDate":"2024-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140835568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-24DOI: 10.1109/TNB.2024.3386586
P. Harika;Girija Sravani Kondavitee;Srinivasa Rao Karumuri;Aime Lay-Ekuakille
The Dielectrically Modulated Full Gate Tunnel Field Effect Transistor (FET) with dual nanocavities, as described in the paper, is a novel device designed as a label-free biosensor for detecting cancer cell biomolecules. This biosensor utilizes the principles of field-effect transistors and incorporates nanocavities to enhance the detection sensitivity. The simulations are conducted using the Silvaco Atlas model, which allowed for the analysis of the device’s electrical characteristics in the presence of various cancer cell biomolecules. The performance of the proposed device is evaluated using several sensing metrics, including current, threshold voltage, and subthreshold slope. These metrics are examined to assess their sensitivity to the presence of different cancer cell biomolecules. By analyzing these electrical characteristics, we can able to determine the device’s ability to detect and differentiate between specific biomolecules associated with cancer cells. One important aspect discussed in the paper is the incorporation of nanocavities in the device design. These nanocavities have a significant impact on enhancing the sensing capabilities of the biosensor. The paper also introduces the concept of the filling factor parameter, which describes the fraction of the nanocavity volume occupied by the cancer cell biomolecules. This parameter plays a crucial role in achieving optimal sensing performance. Overall, the paper presents a comprehensive analysis of the proposed Dielectrically Modulated Full gate Tunnel FET embedded with dual nanocavities as a label-free biosensor for cancer cell biomolecules. The simulations conducted using the Silvaco Atlas model provide valuable insights into the device’s electrical characteristics and its sensitivity to different biomolecules. The study emphasizes the significance of nanocavities and their filling factor parameter in achieving enhanced sensing performance for cancer cell detection.
{"title":"High Sensitivity of Dielectrically Modulated Tunnel Field Effect Transistor for Biosensor Applications","authors":"P. Harika;Girija Sravani Kondavitee;Srinivasa Rao Karumuri;Aime Lay-Ekuakille","doi":"10.1109/TNB.2024.3386586","DOIUrl":"10.1109/TNB.2024.3386586","url":null,"abstract":"The Dielectrically Modulated Full Gate Tunnel Field Effect Transistor (FET) with dual nanocavities, as described in the paper, is a novel device designed as a label-free biosensor for detecting cancer cell biomolecules. This biosensor utilizes the principles of field-effect transistors and incorporates nanocavities to enhance the detection sensitivity. The simulations are conducted using the Silvaco Atlas model, which allowed for the analysis of the device’s electrical characteristics in the presence of various cancer cell biomolecules. The performance of the proposed device is evaluated using several sensing metrics, including current, threshold voltage, and subthreshold slope. These metrics are examined to assess their sensitivity to the presence of different cancer cell biomolecules. By analyzing these electrical characteristics, we can able to determine the device’s ability to detect and differentiate between specific biomolecules associated with cancer cells. One important aspect discussed in the paper is the incorporation of nanocavities in the device design. These nanocavities have a significant impact on enhancing the sensing capabilities of the biosensor. The paper also introduces the concept of the filling factor parameter, which describes the fraction of the nanocavity volume occupied by the cancer cell biomolecules. This parameter plays a crucial role in achieving optimal sensing performance. Overall, the paper presents a comprehensive analysis of the proposed Dielectrically Modulated Full gate Tunnel FET embedded with dual nanocavities as a label-free biosensor for cancer cell biomolecules. The simulations conducted using the Silvaco Atlas model provide valuable insights into the device’s electrical characteristics and its sensitivity to different biomolecules. The study emphasizes the significance of nanocavities and their filling factor parameter in achieving enhanced sensing performance for cancer cell detection.","PeriodicalId":13264,"journal":{"name":"IEEE Transactions on NanoBioscience","volume":"24 1","pages":"25-36"},"PeriodicalIF":3.7,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140663155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-16DOI: 10.1109/TNB.2024.3385413
Chi Ma;Mengnan Zhang;Fei Teng;Wei Zheng;Yan Mi
Pulsed magnetic field treatment can enhance cell membrane permeability, allowing large molecular substances that normally cannot pass through the cell membrane to enter the cell. This research holds significant prospects for biomedical applications. However, the mechanism underlying pulsed magnetic field-induced cell permeabilization remains unclear, impeding further progress in research related to pulsed magnetic field. Currently, hypotheses about the mechanism are struggling to explain experimental results. Therefore, this study developed a parameter-adjustable pulsed magnetic field generator and designed experiments. Starting from the widely accepted hypothesis of “induced electric fields by pulsed magnetic field,” we conducted a preliminary exploration of the biophysical mechanisms underlying pulsed magnetic field-induced cell permeabilization. Finally, we have arrived at an intriguing conclusion: under the current technical parameters, the impact of the pulsed magnetic field itself is the primary factor influencing changes in cell membrane permeability, rather than the induced electric field. This conclusion holds significant implications for understanding the biophysical mechanisms behind pulsed magnetic field therapy and its potential biomedical applications.
{"title":"Preliminary Exploration of the Biophysical Mechanisms of Pulsed Magnetic Field- Induced Cell Permeabilization","authors":"Chi Ma;Mengnan Zhang;Fei Teng;Wei Zheng;Yan Mi","doi":"10.1109/TNB.2024.3385413","DOIUrl":"10.1109/TNB.2024.3385413","url":null,"abstract":"Pulsed magnetic field treatment can enhance cell membrane permeability, allowing large molecular substances that normally cannot pass through the cell membrane to enter the cell. This research holds significant prospects for biomedical applications. However, the mechanism underlying pulsed magnetic field-induced cell permeabilization remains unclear, impeding further progress in research related to pulsed magnetic field. Currently, hypotheses about the mechanism are struggling to explain experimental results. Therefore, this study developed a parameter-adjustable pulsed magnetic field generator and designed experiments. Starting from the widely accepted hypothesis of “induced electric fields by pulsed magnetic field,” we conducted a preliminary exploration of the biophysical mechanisms underlying pulsed magnetic field-induced cell permeabilization. Finally, we have arrived at an intriguing conclusion: under the current technical parameters, the impact of the pulsed magnetic field itself is the primary factor influencing changes in cell membrane permeability, rather than the induced electric field. This conclusion holds significant implications for understanding the biophysical mechanisms behind pulsed magnetic field therapy and its potential biomedical applications.","PeriodicalId":13264,"journal":{"name":"IEEE Transactions on NanoBioscience","volume":"23 3","pages":"482-490"},"PeriodicalIF":3.7,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140612766","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-08DOI: 10.1109/TNB.2024.3385739
Yuvraj Maphrio Mao;Khairunnisa Amreen;Sanket Goel
Microbial Fuel Cells (MFCs) have recently gained attention, as they are inexpensive, green in nature, and sustainable. As per the report, by Allied Market Research the global market size of MFCs will increase from ${$}$ 264.8 million in 2021 to ${$}$ 452.2 million in 2030, growing at a CAGR of 4.5%. The present work is a comparative study of various types of electrolytes that can be used in MFCs. The working electrodes were printed using conducting graphene-based Polylactic Acid (PLA) filaments with the help of a 3D printer under the principle of the fused deposition method. Simulated electrolytes and natural environmental microbial electrolytes were used here. Also, electrolytes of pure E. coli culture were studied. Lake water reported the highest power density of 8.259 mW/cm2 while Stale E. Coli reported the lowest around 0.184 mW/cm2. The study comprehensively lists potential wastewaters that can fuel the MFCs. With the pioneering of various comparative studies of electrolytes, one can insight into the recruitment of electrolytes with high-performance benchmarks for miniaturized energy storage and other microelectronics applications.
{"title":"Benchmarking Power Generation From Multiple Wastewater Electrolytes in Microbial Fuel Cells With 3D Printed Disk-Electrodes","authors":"Yuvraj Maphrio Mao;Khairunnisa Amreen;Sanket Goel","doi":"10.1109/TNB.2024.3385739","DOIUrl":"10.1109/TNB.2024.3385739","url":null,"abstract":"Microbial Fuel Cells (MFCs) have recently gained attention, as they are inexpensive, green in nature, and sustainable. As per the report, by Allied Market Research the global market size of MFCs will increase from \u0000<inline-formula> <tex-math>${$}$ </tex-math></inline-formula>\u0000 264.8 million in 2021 to \u0000<inline-formula> <tex-math>${$}$ </tex-math></inline-formula>\u0000 452.2 million in 2030, growing at a CAGR of 4.5%. The present work is a comparative study of various types of electrolytes that can be used in MFCs. The working electrodes were printed using conducting graphene-based Polylactic Acid (PLA) filaments with the help of a 3D printer under the principle of the fused deposition method. Simulated electrolytes and natural environmental microbial electrolytes were used here. Also, electrolytes of pure E. coli culture were studied. Lake water reported the highest power density of 8.259 mW/cm2 while Stale E. Coli reported the lowest around 0.184 mW/cm2. The study comprehensively lists potential wastewaters that can fuel the MFCs. With the pioneering of various comparative studies of electrolytes, one can insight into the recruitment of electrolytes with high-performance benchmarks for miniaturized energy storage and other microelectronics applications.","PeriodicalId":13264,"journal":{"name":"IEEE Transactions on NanoBioscience","volume":"23 3","pages":"491-498"},"PeriodicalIF":3.7,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140570103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-02DOI: 10.1109/TNB.2024.3384082
Xuewen Qian;Stefan Angerbauer;Malcolm Egan;Marco Di Renzo;Werner Haselmayr
A challenge for real-time monitoring of biochemical processes, such as cells, is detection of biologically relevant molecules. This is due to the fact that spectroscopy methods for detection may perturb the cellular environment. One approach to overcome this problem is coupled microfluidic-spectroscopy, where a microfluidic output channel is introduced in order to observe biologically relevant molecules. This approach allows for non-passive spectroscopy methods, such as mass spectrometry, to identify the structure of molecules released by the cell. Due to the non-negligible length of the microfluidic channel, when a sequence of stimuli are applied to a cell it is not straightforward to determine which spectroscopy samples correspond to a given stimulus. In this paper, we propose a solution to this problem by taking a molecular communication (MC) perspective on the coupled microfluidic-spectroscopy system. In particular, assignment of samples to a stimulus is viewed as a synchronization problem. We develop two new algorithms for synchronization in this context and carry out a detailed theoretical and numerical study of their performance. Our results show improvements over maximum-likelihood synchronization algorithms in terms of detection performance when there are uncertainties in the composition of the microfluidic channel.
{"title":"A Molecular Communication Perspective on Synchronization of Coupled Microfluidic-Spectroscopy","authors":"Xuewen Qian;Stefan Angerbauer;Malcolm Egan;Marco Di Renzo;Werner Haselmayr","doi":"10.1109/TNB.2024.3384082","DOIUrl":"10.1109/TNB.2024.3384082","url":null,"abstract":"A challenge for real-time monitoring of biochemical processes, such as cells, is detection of biologically relevant molecules. This is due to the fact that spectroscopy methods for detection may perturb the cellular environment. One approach to overcome this problem is coupled microfluidic-spectroscopy, where a microfluidic output channel is introduced in order to observe biologically relevant molecules. This approach allows for non-passive spectroscopy methods, such as mass spectrometry, to identify the structure of molecules released by the cell. Due to the non-negligible length of the microfluidic channel, when a sequence of stimuli are applied to a cell it is not straightforward to determine which spectroscopy samples correspond to a given stimulus. In this paper, we propose a solution to this problem by taking a molecular communication (MC) perspective on the coupled microfluidic-spectroscopy system. In particular, assignment of samples to a stimulus is viewed as a synchronization problem. We develop two new algorithms for synchronization in this context and carry out a detailed theoretical and numerical study of their performance. Our results show improvements over maximum-likelihood synchronization algorithms in terms of detection performance when there are uncertainties in the composition of the microfluidic channel.","PeriodicalId":13264,"journal":{"name":"IEEE Transactions on NanoBioscience","volume":"23 3","pages":"458-471"},"PeriodicalIF":3.7,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140570107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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