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Recent Submissions

  • Enhanced linearity through high-order antisymmetric vibration for MEMS DC power sensor

    zou, xuecui; Jaber, Nizar; Bu Khamsin, Abdullah; Yaqoob, Usman; Salama, Khaled N.; Fariborzi, Hossein (Applied Physics Letters, AIP Publishing, 2023-09-13) [Article]
    We present an electric power meter that capitalizes on the interaction of electrothermal strain and mechanical vibration in a micro-electro-mechanical systems (MEMS) beam undergoing the antisymmetric mode of vibration. This is achieved by using a resonant bridge driven with an electrothermal modulation technique. The change in electrical power is monitored through the alteration in the mechanical stiffness of the structure, which is tracked electrostatically. The observed deflection profile of the beam under the influence of electrothermal effects shows that the deflection geometry due to buckling exhibits similar trends as the first symmetric vibrational mode, in contrast to the antisymmetric mode. Therefore, we compare two distinct vibrational modes, converting the compressive thermal stress generated by the input electrical power via Joule heating into a shift in the resonance frequency. By employing antisymmetric vibrational mode, the output of our device is consistently monotonic to the input electrical power, even when the microbeam is experiencing buckling deflections. In addition, the sensing operation based on antisymmetric modes yields only a 1.5% nonlinear error in the response curve, which is ten times lower than that of symmetric modes. The observed deformation shape of the resonator agrees with the results obtained from multi-physics finite simulations. Finally, this approach has the potential to be extended to other frequency-shift-based sensors, allowing for higher linearity.
  • Self-powered Flexible Wearable Sensing Platform for Ascorbic Acid Detection in Sweat

    De Oliveira Filho, José Ilton; Ferreira, Daísy Camargo; Faleiros, Murilo Calil; Salama, Khaled N. (IEEE Sensors Journal, Institute of Electrical and Electronics Engineers (IEEE), 2023-09-06) [Article]
    Laser-scribed graphene electrodes (LSGEs) are an excellent platform for biosensors due to their electronic properties, large surface area, and high porosity that enhances the electron transfer rate. The flexibility of LSGEs combined with the advantages of electrochemical techniques for a portable device is very suitable for wearable applications. We report a lightweight (<2 grams) wearable platform paired with non-modified LSGE to detect ascorbic acid (AA) in sweat. Electrochemical techniques such as square wave voltammetry and chronoamperometry were used to acquire the data. The acquired data are transmitted wirelessly to the user via Bluetooth or through UART using a custom-made application. The limits of detection (LOD) achieved were 66 μM and 4.7 μM for square wave voltammetry and chronoamperometry respectively. Furthermore, a self-powered feature is demonstrated in which the device can be powered by harvesting energy from ambient light.
  • Metal–organic frameworks modified electrode for H2S detections in biological and pharmaceutical agents

    Durmus, Ceren; Arul, Ponnusamy; Alhaji, Abdulhadi; Shekhah, Osama; Mani, Veerappan; Eddaoudi, Mohamed; Salama, Khaled N. (MedComm – Biomaterials and Applications, Wiley, 2023-08-20) [Article]
    The development of hydrogen sulfide (H2S) sensors is essential to address H2S-related pharmacology since slow-releasing H2S medications have been identified to be prospective options for cancer treatments. Here, we described an electrochemical sensor for highly selective and sensitive detection of aqueous H2S, using a thin film of fumarate-based face-centered cubic (fcu)-based metal–organic frameworks (fum-fcu-MOF) modified on laser-scribed graphene (LSGE). The fum-fcu-MOF has shown a strong affinity and chemical stability to H2S analysis. The electrochemical and H2S catalytic properties were studied for fum-fcu-MOF/LSGE. An amperometry and differential pulse voltammetry techniques were demonstrated to validate the sensor. The resulting sensor delivered acceptable analytical parameters in terms of; detection limit (3.0 µM), dynamic range (10–500 µM), reproducibility, and stability (94.7%). The sensor's practical validity was demonstrated in bacterial cells and H2S-releasing drug, where the sensor was able to monitor the continuous release of in-situ H2S. The pharmacokinetics of a slow releasing H2S donor is accessed at different time intervals and different concentration levels. Our research indicate that this fum-fcu-MOF based H2S sensor holds potential in understanding pharmacokinetics of H2S releasing drugs.
  • An End-To-End Neuromorphic Radio Classification System with an Efficient Sigma-Delta-Based Spike Encoding Scheme

    Guo, Wenzhe; Yang, Kuilian; Stratigopoulos, Haralampos-G.; Aboushady, Hassan; Salama, Khaled N. (IEEE Transactions on Artificial Intelligence, Institute of Electrical and Electronics Engineers (IEEE), 2023-08-18) [Article]
    Rapid advancements in 5G communication and the Internet of Things have prompted the development of cognitive radio sensing for spectrum monitoring and malicious attack detection. An end-to-end radio classification system is essential to realize efficient real-time monitoring at the edge. This work presents an end-to-end neuromorphic system enabled by an efficient spiking neural network (SNN) for radio classification. A novel hardware-efficient spiking encoding method is proposed leveraging the Sigma-Delta modulation mechanism in analog-to-digital converters. It requires no additional hardware components, simplifies the system design, and helps reduce conversion latency. Following a designed hardware-emulating conversion process, the classification performance is verified on two benchmark radio modulation datasets. A comparable accuracy to an artificial neural network (ANN) baseline with a difference of 0.30% is achieved on the dataset RADIOML 2018 with more realistic conditions. Further analysis reveals that the proposed method requires less power-intensive computational operations, leading to 22× lower computational energy consumption. Additionally, this method exhibits more than 99% accuracy on the dataset when the signal-to-noise ratio is above zero dB. The SNN-based classification module is realized on FPGA with a heterogeneous streaming architecture, achieving high throughput and low resource utilization. Therefore, this work demonstrates a promising solution for constructing an efficient high-performance end-to-end radio classification system.
  • Empowering Electrochemical Biosensors with AI: Overcoming Interference for Precise Dopamine Detection in Complex Samples

    De Oliveira Filho, José Ilton; Faleiros, Murilo Calil; Ferreira, Daisy Camargo; Mani, Veerappan; Salama, Khaled N. (Advanced Intelligent Systems, Wiley, 2023-08-18) [Article]
    Two significant issues in biosensors that can't be solved by conventional analytical methods are selectivity among likely biological interfering molecules and background noise in clinical samples. The application of embedded machine learning in the removal of background interference, which is extremely common in complex matrix solutions such as cerebrospinal fluid, is still unexplored. The implementation of machine learning into devices and sensors can enhance their reliability to discriminate responses. In addition, implementing these models into portable devices can further improve the usage of point-of-care devices, continuous monitoring, and viral mutation assessments. Herein, the requirements to implement an embedded AI model (TinyML) into low-power portable systems and its use in the electrochemical field are presented. The application of TinyML to discriminate between interference of uric acid and ascorbic acid, both well-known abundant electrochemically active species, in neurotransmitter detection, is explored, reaching overall accuracy of 98.1% for 32-bit float point unit and 96.01% after 8-bit quantization, with the usage of 4.55% of the custom-made potentiostat memory. The model can be improved simply by having the trade-off between memory and accuracy. The research suggests that TinyML can be a key component in future medical devices, allowing data processing in real time and with increasing reliability.
  • Molecularly Imprinted Polymers: A Closer Look at the Removal and Rebinding of Templates

    Lamaoui, Abderrahman; Mani, Veerappan; Durmus, Ceren; Salama, Khaled N.; Amine, Aziz (Elsevier BV, 2023-08-15) [Preprint]
    Molecularly imprinted polymers (MIPs), which first appeared three decades ago, are now attracting considerable attention as artificial receptors, particularly for sensing. MIPs, especially applied to biomedical analysis in biofluids, contribute significantly to patient diagnosis at the point of care, thereby allowing health monitoring. Despite the importance given to MIPs, removal and rebinding of templates have received little attention and are currently the least focused steps in MIP development. The primary focus of this review is to analyze and discuss the advanced ideas on this topic. The review aims to provide an overview of the removal and rebinding of different templates including ions, molecules, proteins, viruses, and bacteria. Furthermore, the current challenges and perspectives in removal and rebinding processes are highlighted. Our review, at the interface of chemistry and sensors, will offer a wide range of opportunities for researchers whose interests include MIPs, (bio)sensors, analytical chemistry, and diagnostics.
  • Smart Multiplex Point-of-Care Platform for Simultaneous Drug Monitoring

    Beduk, Duygu; Beduk, Tutku; De Oliveira Filho, José Ilton; Ait Lahcen, Abdellatif; Aldemir, Ebru; Guler Celik, Emine; Salama, Khaled N.; Timur, Suna (ACS Applied Materials & Interfaces, American Chemical Society (ACS), 2023-07-27) [Article]
    Recently, illicit drug use has become more widespread and is linked to problems with crime and public health. These drugs disrupt consciousness, affecting perceptions and feelings. Combining stimulants and depressants to suppress the effect of drugs has become the most common reason for drug overdose deaths. On-site platforms for illicit-drug detection have gained an important role in dealing, without any excess equipment, long process, and training, with drug abuse and drug trafficking. Consequently, the development of rapid, sensitive, noninvasive, and reliable multiplex drug-detecting platforms has become a major necessity. In this study, a multiplex laser-scribed graphene (LSG) sensing platform with one counter, one reference, and three working electrodes was developed for rapid and sensitive electrochemical detection of amphetamine (AMP), cocaine (COC), and benzodiazepine (BZD) simultaneously in saliva samples. The multidetection sensing system was combined with a custom-made potentiostat to achieve a complete point-of-care (POC) platform. Smartphone integration was achieved by a customized application to operate, display, and send data. To the best of our knowledge, this is the first multiplex LSG-based electrochemical platform designed for illicit-drug detection with a custom-made potentiostat device to build a complete POC platform. Each working electrode was optimized with standard solutions of AMP, COC, and BZD in the concentration range of 1.0 pg/mL-500 ng/mL. The detection limit of each illicit drug was calculated as 4.3 ng/mL for AMP, 9.7 ng/mL for BZD, and 9.0 ng/mL for COC. Healthy and MET (methamphetamine) patient saliva samples were used for the clinical study. The multiplex LSG sensor was able to detect target analytes in real saliva samples successfully. This multiplex detection device serves the role of a practical and affordable alternative to conventional drug-detection methods by combining multiple drug detections in one portable platform.
  • Towards Efficient In-memory Computing Hardware for Quantized Neural Networks: State-of-the-art, Open Challenges and Perspectives

    Krestinskaya, Olga; Zhang, Li; Salama, Khaled N. (IEEE Transactions on Nanotechnology, Institute of Electrical and Electronics Engineers (IEEE), 2023-07-06) [Article]
    The amount of data processed in the cloud, the development of Internet-of-Things (IoT) applications, and growing data privacy concerns force the transition from cloud-based to edge-based processing. Limited energy and computational resources on edge push the transition from traditional von Neumann architectures to In-memory Computing (IMC), especially for machine learning and neural network applications. Network compression techniques are applied to implement a neural network on limited hardware resources. Quantization is one of the most efficient network compression techniques allowing to reduce the memory footprint, latency, and energy consumption. This paper provides a comprehensive review of IMC-based Quantized Neural Networks (QNN) and links software-based quantization approaches to IMC hardware implementation. Moreover, open challenges, QNN design requirements, recommendations, and perspectives along with an IMC-based QNN hardware roadmap are provided.
  • Fabrication and Characterization of Porous Microneedles for Enhanced Fluid Injection and Suction: A Two-Photon Polymerization Approach

    Fakeih, Esraa; Aguirre-Pablo, Andres A.; Thoroddsen, Sigurdur T; Salama, Khaled N. (Advanced Engineering Materials, Wiley, 2023-06-29) [Article]
    Porous Microneedles (MNs) offer broad advantages such as fluid capture and filtration. Compared to hollow MNs, fluid injection through porous MNs causes a broader diffusion spread. In this paper, we fabricated and compared three MN designs with a constant pore size and controlled pore locations, using two-photon polymerization (2PP), by examining factors such as diffusion spread, mixing capabilities, and mechanical resilience. Results show that the porous MN can cover 16 times the injection area than that of the hollow MN. Porous MNs also showed good mixing capabilities with two fluids. Mechanical compression results revealed that one porous MN could withstand a load of 0.6 N.
  • Signal Processing Circuits and Systems for Smart Sensing Applications

    Herencsar, Norbert; Salama, Khaled N. (Sensors, MDPI AG, 2023-06-10) [Article]
  • Highly Sensitive Wireless NO2 Gas Sensing System

    Bizak, Zhanibek; Faleiros, Murilo Calil; Vijjapu, Mani Teja; Yaqoob, Usman; Salama, Khaled N. (IEEE Sensors Journal, Institute of Electrical and Electronics Engineers (IEEE), 2023-06-05) [Article]
    There is a need for reliable and accurate air quality monitoring systems in order to secure human health from the air pollutants such as nitrogen dioxide (NO 2 ). The development of low-cost and modern technology-compatible sensing systems is essential. In this work, a highly sensitive and selective Indium Gallium Zinc oxide (IGZO) thin film transistor has been used to design an efficient and wireless NO 2 sensing system. The sensing system uses the IGZO TFT as a current source for the current-starved ring oscillator where exposed NO 2 concentration determines the oscillation frequency. The presented NO 2 gas sensing system exhibited excellent sensitivity of 5.39 MHz/ppm with the lowest detection limit (LOD) of 50 ppb at room temperature. To the best of the authors’ knowledge, the achieved sensitivity is the best-reported performance for frequency-based NO 2 sensors. The sinusoidal output of the sensor obviates the need for costly peripheral signal conditioning circuits and allows direct integration with wireless systems. The 13.56 MHz RFID antenna connected to the output of the sensor is used to show wireless sensing compatibility and highlights the excellent candidacy of the proposed work for the growing Internet of Things (IoT) and untethered sensor applications.
  • Resistive Neural Hardware Accelerators

    Smagulova, Kamilya; Fouda, Mohamed E.; Kurdahi, Fadi; Salama, Khaled N.; Eltawil, Ahmed (Institute of Electrical and Electronics Engineers (IEEE), 2023-05-16) [Article]
    Deep neural networks (DNNs), as a subset of machine learning (ML) techniques, entail that real-world data can be learned, and decisions can be made in real time. However, their wide adoption is hindered by a number of software and hardware limitations. The existing general-purpose hardware platforms used to accelerate DNNs are facing new challenges associated with the growing amount of data and are exponentially increasing the complexity of computations. Emerging nonvolatile memory (NVM) devices and the compute-in-memory (CIM) paradigm are creating a new hardware architecture generation with increased computing and storage capabilities. In particular, the shift toward resistive random access memory (ReRAM)-based in-memory computing has great potential in the implementation of area- and power-efficient inference and in training large-scale neural network architectures. These can accelerate the process of IoT-enabled AI technologies entering our daily lives. In this survey, we review the state-of-the-art ReRAM-based DNN many-core accelerators, and their superiority compared to CMOS counterparts was shown. The review covers different aspects of hardware and software realization of DNN accelerators, their present limitations, and prospects. In particular, a comparison of the accelerators shows the need for the introduction of new performance metrics and benchmarking standards. In addition, the major concerns regarding the efficient design of accelerators include a lack of accuracy in simulation tools for software and hardware codesign.
  • CD34+ HSPCs-derived exosomes contain dynamic cargo and promote their migration through functional binding with the homing receptor E-selectin

    Isaioglou, Ioannis; Aldehaiman, Mansour M.; Li, Yanyan; Lahcen, Abdellatif Ait; Rauf, Sakandar; Al-Amoodi, Asma S.; Habiba, Umme; Alghamdi, Abdullah; Nozue, Shuho; Habuchi, Satoshi; Salama, Khaled N.; Merzaban, Jasmeen (Frontiers in Cell and Developmental Biology, Frontiers Media SA, 2023-04-25) [Article]
    Exosomes are tiny vesicles released by cells that carry communications to local and distant locations. Emerging research has revealed the role played by integrins found on the surface of exosomes in delivering information once they reach their destination. But until now, little has been known on the initial upstream steps of the migration process. Using biochemical and imaging approaches, we show here that exosomes isolated from both leukemic and healthy hematopoietic stem/progenitor cells can navigate their way from the cell of origin due to the presence of sialyl Lewis X modifications surface glycoproteins. This, in turn, allows binding to E-selectin at distant sites so the exosomes can deliver their messages. We show that when leukemic exosomes were injected into NSG mice, they traveled to the spleen and spine, sites typical of leukemic cell engraftment. This process, however, was inhibited in mice pre-treated with blocking E-selectin antibodies. Significantly, our proteomic analysis found that among the proteins contained within exosomes are signaling proteins, suggesting that exosomes are trying to deliver active cues to recipient cells that potentially alter their physiology. Intriguingly, the work outlined here also suggests that protein cargo can dynamically change upon exosome binding to receptors such as E-selectin, which thereby could alter the impact it has to regulate the physiology of the recipient cells. Furthermore, as an example of how miRNAs contained in exosomes can influence RNA expression in recipient cells, our analysis showed that miRNAs found in KG1a-derived exosomes target tumor suppressing proteins such as PTEN.
  • A Mechanical Memory with Capacitance Modulation of Nonlinear Resonance

    zou, xuecui; Yang, Yiming; Yaqoob, Usman; Shamim, Atif; Salama, Khaled N.; Fariborzi, Hossein (IEEE Electron Device Letters, Institute of Electrical and Electronics Engineers (IEEE), 2023-04-19) [Article]
    We introduce a novel and straightforward technique for modulating the nonlinear dynamics of a micro-beam by utilizing multiple structural capacitances developed through I/O electrode configurations. Our findings show that the capacitive modulation technique significantly enhances the nonlinear motion of the resonator. By exciting one or more capacitance paths with the same input AC signals, the nonlinear resonance can be actuated, leading to effective modulation of the nonlinear frequency region. The results show that the nonlinear resonance characteristics with capacitance modulation enable two controllable states observed at the resonator output, facilitating memory operations with unified input and output waveforms. In the experiment, we demonstrate that by switching (on/off) capacitance paths, the nonlinear resonance can be effectively modulated, resulting in a clear transition between memory states. The paradigm demonstrated in this study utilizes structural capacitance modulation, providing an energy-efficient and straightforward solution for mechanical memory designs, while also making it easier to be manufactured and integrated into electronic systems.
  • Efficient training of spiking neural networks with temporally-truncated local backpropagation through time

    Guo, Wenzhe; Fouda, Mohammed E.; Eltawil, Ahmed; Salama, Khaled N. (Frontiers in Neuroscience, Frontiers Media SA, 2023-04-06) [Article]
    Directly training spiking neural networks (SNNs) has remained challenging due to complex neural dynamics and intrinsic non-differentiability in firing functions. The well-known backpropagation through time (BPTT) algorithm proposed to train SNNs suffers from large memory footprint and prohibits backward and update unlocking, making it impossible to exploit the potential of locally-supervised training methods. This work proposes an efficient and direct training algorithm for SNNs that integrates a locally-supervised training method with a temporally-truncated BPTT algorithm. The proposed algorithm explores both temporal and spatial locality in BPTT and contributes to significant reduction in computational cost including GPU memory utilization, main memory access and arithmetic operations. We thoroughly explore the design space concerning temporal truncation length and local training block size and benchmark their impact on classification accuracy of different networks running different types of tasks. The results reveal that temporal truncation has a negative effect on the accuracy of classifying frame-based datasets, but leads to improvement in accuracy on event-based datasets. In spite of resulting information loss, local training is capable of alleviating overfitting. The combined effect of temporal truncation and local training can lead to the slowdown of accuracy drop and even improvement in accuracy. In addition, training deep SNNs' models such as AlexNet classifying CIFAR10-DVS dataset leads to 7.26% increase in accuracy, 89.94% reduction in GPU memory, 10.79% reduction in memory access, and 99.64% reduction in MAC operations compared to the standard end-to-end BPTT. Thus, the proposed method has shown high potential to enable fast and energy-efficient on-chip training for real-time learning at the edge.
  • An interconnect-free micro-electromechanical 7-bit arithmetic device for multi-operand programmable computing

    zou, xuecui; Yaqoob, Usman; Ahmed, Sally; Wang, Yue; Salama, Khaled N.; Fariborzi, Hossein (Microsystems & Nanoengineering, Springer Science and Business Media LLC, 2023-04-03) [Article]
    Computational power density and interconnection between transistors have grown to be the dominant challenges for the continued scaling of complementary metal–oxide–semiconductor (CMOS) technology due to limited integration density and computing power. Herein, we designed a novel, hardware-efficient, interconnect-free microelectromechanical 7:3 compressor using three microbeam resonators. Each resonator is configured with seven equal-weighted inputs and multiple driven frequencies, thus defining the transformation rules for transmitting resonance frequency to binary outputs, performing summation operations, and displaying outputs in compact binary format. The device achieves low power consumption and excellent switching reliability even after 3 × 103 repeated cycles. These performance improvements, including enhanced computational power capacity and hardware efficiency, are paramount for moderately downscaling devices. Finally, our proposed paradigm shift for circuit design provides an attractive alternative to traditional electronic digital computing and paves the way for multioperand programmable computing based on electromechanical systems.
  • Polyoxometalate-cyclodextrin supramolecular entities for real-time in situ monitoring of dopamine released from neuroblastoma cells

    Shetty, Saptami Suresh; Moosa, Basem; Zhang, Li; Alshankiti, Buthainah; Baslyman, Walaa; Mani, Veerappan; Khashab, Niveen M.; Salama, Khaled N. (Biosensors & bioelectronics, Elsevier BV, 2023-03-23) [Article]
    Optimized and sensitive biomarker detection has recently been shown to have a critical impact on quality of diagnosis and medical care options. In this research study, polyoxometalate-γ-cyclodextrin metal-organic framework (POM-γCD MOF) was utilized as an electrocatalyst to fabricate highly selective sensors to detect in-situ released dopamine. The POM-γCD MOF produced multiple modes of signals for dopamine including electrochemical, colorimetric, and smartphone read-outs. Real-time quantitative monitoring of SH-SY5Y neuroblastoma cellular dopamine production was successfully demonstrated under various stimuli at different time intervals. The POM-CD MOF sensor and linear regression model were used to develop a smartphone read-out platform, which converts dopamine visual signals to digital signals within a few seconds. Ultimately, POM-γCD MOFs can play a significant role in the diagnosis and treatment of various diseases that involve dopamine as a significant biomarker.
  • Supervised Local Training with Backward Links for Deep Neural Networks

    Guo, Wenzhe; Fouda, Mohamed E.; Eltawil, Ahmed; Salama, Khaled N. (IEEE Transactions on Artificial Intelligence, Institute of Electrical and Electronics Engineers (IEEE), 2023-03-02) [Article]
    The restricted training pattern in the standard BP requires end-to-end error propagation, causing large memory costs and prohibiting model parallelization. Existing local training methods aim to resolve the training obstacles by completely cutting off the backward path between modules and isolating their gradients. These methods prevent information exchange between modules and result in inferior performance. This work proposes a novel local training algorithm, BackLink, which introduces inter-module backward dependency and facilitates information to flow backward along with the network. To preserve the computational advantage of local training, BackLink restricts the error propagation length within the module. Extensive experiments performed in various deep convolutional neural networks demonstrate that our method consistently improves the classification performance of local training algorithms over other methods. For example, our method can surpass the conventional greedy local training method by 6.45% in accuracy in ResNet32 classifying CIFAR100 and recent work by 2.58% in ResNet110 classifying STL-10 with much lower complexity, respectively. Analysis of computational costs reveals that small overheads are incurred in GPU memory costs and runtime on multiple GPUs. Our method can lead up to a 79% reduction in memory cost and 52% in simulation runtime in ResNet110 compared to the standard BP. Therefore, our method could create new opportunities for improving training algorithms towards better efficiency for real-time learning applications.
  • Iron Single-Atom Catalysts on MXenes for Ultrasensitive Monitoring of Adrenal Tumor Markers and Cellular Dopamine

    Shetty, Saptami; El Demellawi, Jehad K.; Khan, Yusuf; Hedhili, Mohamed N.; Arul, Ponnusamy; Mani, Veerappan; Alshareef, Husam N.; Salama, Khaled N. (Advanced Materials Technologies, Wiley, 2023-02-02) [Article]
    Neuroblastoma and pheochromocytoma are the most prevalent malignancies of the adrenal medulla. They are currently diagnosed by measuring urinary catecholamines using high-performance liquid chromatography-mass spectrometry, which is expensive, bulky, and tedious. Electrochemical detectors stand out as low-cost alternatives; however, further development of functional materials with adequate sensitivity is still required to attain clinically useful diagnostic devices. Here, Ti3C2Tx MXene nanosheets stabilized with iron single-atom catalysts (Fe-SACs), anchored on the surface, are synthesized and utilized as efficient electrocatalysts for the determination of catecholamine (dopamine (DA)) and its end-products (vanillylmandelic acid (VMA) and homovanillic acid (HVA)). The Fe-SACs/Ti3C2Tx exhibits low oxidation overpotentials with high signal amplifications up to 610%, 290%, and 420%, and sensitive detection limits of 1.0, 5.0, and 10 nM for DA, VMA, and HVA, respectively. The presence of the atomic Fe elements on the Ti3C2Tx nanosheets is confirmed using high-resolution scanning transmission electron microscopy and X-ray photoelectron spectroscopy. The Fe-SACs/Ti3C2Tx sensor tracks the in situ production of DA in PC12 cells and found practically useful in analyzing human urine samples. The Fe-SACs/Ti3C2Tx stands out as a sensitive diagnostic platform for evaluating the progression of tumors and the quality of cellular DA communications
  • Institution of Metal–Organic Frameworks as a Highly Sensitive and Selective Layer In-Field Integrated Soil-Moisture Capacitive Sensor

    Alsadun, Norah Sadun; Surya, Sandeep Goud; Patle, Kamlesh; Palaparthy, Vinay S.; Shekhah, Osama; Salama, Khaled N.; Eddaoudi, Mohamed (ACS Applied Materials & Interfaces, American Chemical Society (ACS), 2023-01-20) [Article]
    The ongoing global industrialization along with the notable world population growth is projected to challenge the global environment as well as pose greater pressure on water and food needs. Foreseeably, an improved irrigation management system is essential and the quest for refined chemical sensors for soil-moisture monitoring is of tremendous importance. Nevertheless, the persisting challenge is to design and construct stable materials with the requisite sensitivity, selectivity, and high performance. Here, we report the introduction of porous metal–organic frameworks (MOFs), as the receptor layer, in capacitive sensors to efficiently sense moisture in two types of soil. Namely, our study unveiled that Cr-soc-MOF-1 offers the best sensitivity (≈24,000 pF) among the other tested MOFs for any given range of soil-moisture content, outperforming several well-known oxide materials. The corresponding increase in the sensitivities for tested MOFs at 500 Hz are ≈450, ≈200, and ≈30% for Cr-soc-MOF-1, Al-ABTC-soc-MOF, and Zr-fum-fcu-MOF, respectively. Markedly, Cr-soc-MOF-1, with its well-known water capacity, manifests an excellent sensitivity of ≈450% in clayey soil, and the analogous response time was 500 s. The noted unique sensing properties of Cr-soc-MOF-1 unveils the great potential of MOFs for soil-moisture sensing application.

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