Point-of-care test system for detection of immunoglobulin-G and -M against nucleocapsid protein and spike glycoprotein of SARS-CoV-2

Machine Learning Driven Atom-Thin Materials for Fragrance Sensing

Fragrance plays a crucial role in the daily lives. Its importance spans various sectors, from therapeutic purposes to personal care, making the understanding and accurate identification of fragrances essential. To fully harness the potential of fragrances, efficient and precise fragrance sensing and identification are necessary. However, current fragrance sensors face several limitations, particularly in detecting and differentiating complex scent profiles with high accuracy. To address these challenges, the use of atom-thin materials in fragrance sensors has emerged as a groundbreaking approach. These atom-thin sensors, characterized by their enhanced sensitivity and selectivity, offer significant improvements over traditional sensing technology. Moreover, the integration of Machine Learning (ML) into fragrance sensing has opened new opportunities in the field. ML algorithms applied to fragrance sensing facilitate advancements in four key domains: accurate fragrance identification, precise discrimination between different fragrances, improved detection thresholds for subtle scents, and prediction of fragrance properties. This comprehensive review delves into the synergistic use of atom-thin materials and ML in fragrance sensing, providing an in-depth analysis of how these technologies are revolutionizing the field, offering insights into their current applications and future potential in enhancing the understanding and utilization of fragrances.

Smart mid-infrared metasurface microspectrometer gas sensing system

Smart, low-cost and portable gas sensors are highly desired due to the importance of air quality monitoring for environmental and defense-related applications. Traditionally, electrochemical and nondispersive infrared (IR) gas sensors are designed to detect a single specific analyte. Although IR spectroscopy-based sensors provide superior performance, their deployment is limited due to their large size and high cost. In this study, a smart, low-cost, multigas sensing system is demonstrated consisting of a mid-infrared microspectrometer and a machine learning algorithm. The microspectrometer is a metasurface filter array integrated with a commercial IR camera that is consumable-free, compact ( ~ 1 cm3) and lightweight ( ~ 1 g). The machine learning algorithm is trained to analyze the data from the microspectrometer and predict the gases present. The system detects the greenhouse gases carbon dioxide and methane at concentrations ranging from 10 to 100% with 100% accuracy. It also detects hazardous gases at low concentrations with an accuracy of 98.4%. Ammonia can be detected at a concentration of 100 ppm. Additionally, methyl-ethyl-ketone can be detected at its permissible exposure limit (200 ppm); this concentration is considered low and nonhazardous. This study demonstrates the viability of using machine learning with IR spectroscopy to provide a smart and low-cost multigas sensing platform.

Crystal Face-Dependent Behavior of Single-Atom Pt: Construct of SA-FLP Dual Active Sites for Efficient NO2 Detection

The strong potential of platinum single atom (PtSA) in gas sensor technology is primarily attributed to its high atomic economy. Nevertheless, it is imperative to conduct further exploration to understand the impact of PtSA on the active sites. In this study, the evolution of PtSA on (100)CeO2 and (111)CeO2 is examined, revealing notable disparities in the position and activity of surface PtSA on different crystal planes. The PtSA in (100)CeO2 surface can enhance the stability of Ce3+ and construct a frustrated Lewis pair (FLP) to form a double active site by combining the steric hindrance effect of oxygen vacancies, which increases the response value from 1.8 to 27 and reduce the response-recovery time from 140-192 s to 25-26 s toward five ppm NO2 at room temperature. Conversely, PtSA tends to bind to terminal oxygen on the surface of (111)CeO2 and become an independent reaction site. The response value of PtSA-(111)CeO2 surface only increased from 1.6 to 3.8. This research underscores the correlation between single atoms and crystal plane effects, laying the groundwork for designing and synthesizing ultra-stable and efficient gas sensors.

Molecularly imprinted polymer nanoparticle-carbon nanotube composite electrochemical gas sensor for highly selective and sensitive detection of methanol vapour

An electrochemical gas sensor has been fabricated using molecularly imprinted polymer nanoparticles (nanoMIPs) and multiwalled carbon nanotubes on screen-printed electrodes. Methanol vapour was chosen as the target due to its toxicity as its suitability as a model for more harmful pollutant gases. The sensor functions under ambient conditions and in the required concentration range, in contrast to all previous MIP-based gas sensors for methanol. The sensitivity of the sensor was greatly improved by the addition of multiwall carbon nanotubes, resulting in a limit of detection of approximately 10 ppm. The nanoMIPs provide an inherent selectivity for the target inherent in its design. Selectivity studies were performed with structurally analogous alcohols at various concentrations, demonstrating selectivity for methanol 12.1 times that for ethanol at 2 mmol dm-3 and 4.2 times that for ethanol at 1 mmol dm-3. Interactions with isopropanol and n-propanol were found to be non-specific, and the response to water was negligible. This demonstrates an improvement over previous methanol gas sensors based on molecularly imprinted polymers. No response was observed with carbon nanotubes alone, and no selectivity was observed with non-imprinted equivalents of the nanoMIP sensor. The resulting device is by far the most practical MIP-based instrument for methanol gas sensing thus far described in the literature, being the only example capable of functioning at the necessary methanol vapour concentrations and at the required temperature and humidity. With the selectivity and sensitivity described and the simple design, the developed device provides a substantial advance in the field of molecularly imprinted gas sensors.

A Metal-Free Triazacoronene-Based Bimodal VOC Sensor

Developing suitable sensors for selective and sensitive detection of volatile organic compounds (VOCs) is crucial for monitoring indoor and outdoor air quality. VOCs are very harmful to our health upon inhalation or contact. Bimodal sensor materials with more than one transduction capability (optical and electrical) offer the ability to extract complementary information from the individual analyte, thus improving detection accuracy and performance. The privilege of manipulating the optoelectronic properties of the polycyclic aromatic hydrocarbon-based semiconducting materials offers rapid signal transduction in multimodal sensing applications. A thiophene-functionalized triazacoronene (TTAC) donor-acceptor-donor (D-A-D) type sensor is reported here for VOC sensing. The single-crystal X-ray structure analysis of the TTAC revealed that a distinctive supramolecular polymer architecture was formed because of cooperative π-π and intermolecular D-A interactions and exhibited rapid signal transduction upon exposure to specific VOCs. The TTAC-embedded green luminescent paper-based test strip exhibited an on-off fluorescence response upon nitrobenzene vapor exposure for 120 s. The selective and rapid response is due to the fast photoinduced electron transfer, as is evident from the time-resolved excited-state dynamics and density functional theory studies. The thick-film-based prototype chemiresistive sensor detects harmful VOCs in a custom-made gas sensing system including benzene, toluene, and nitrobenzene. The TTAC sensor rapidly responds (200 s) at relatively low temperatures (180 οC) compared to other reported metal-oxide-based sensors.

A single recognition unit-based virtual sensor Array: Applying 3D fluorescence spectroscopy to inner filter effect-based sensing

A convenient, fast, low-cost detection and discrimination method is demanded for environmental monitoring but still it remains more technological challenges. Herein, we demonstrate that the inner filter effect (IFE), in combination with three-dimensional fluorescence spectroscopy, can offer a virtual sensor array (VSA) as apropersolution. And with the aid of pattern recognition techniques, it is feasible to recognize compounds with structural similarities economically and effectively. In this study, with the help of visual clustering plots of principal component analysis (PCA), a prediction model based on hierarchical strategy was made using support vector machine (SVM) method for the qualitative profiling of aromatic pollutants. The VSA was constructed by a single metal-organic framework (MOF) recognition unit (MOF-74 (Zn)) with the excitation wavelength as external regulatory factors. Pattern characteristics of four aromatics with very similar structures (phenylamine, chlorobenzene, nitrobenzene, and phenol), both single analyte and binary mixtures, were acquired. The primary constituents of multi-dimensional spectral signals were subsequently extracted and fed into a vector machine to construct a prediction model through 10-fold cross-validation optimization, resulting in a classification accuracy of 100% for single analytes and 96% for mixtures. Quantitative research has shown that, except for chlorobenzene, all three other analytes can be predicted in concentration within an acceptable error range, and the mixture can be predicted proportionally. Moreover, the VSA can be used to distinguish these pollutants in tap and river water also. We propose for the first time a new tack for the construction of VSA in a general manner, namely using three-dimensional full range fluorescence scanning for IFE based sensing to get multiple times of information resulting from different weak interaction between analyte and sensor for decision-making.

Potential of Carbon Nanotube Chemiresistor Array in Detecting Gas-Phase Mixtures of Toxic Chemical Compounds

Toxic industrial chemicals (TICs), when accidentally released into the workplace or environment, often form a gaseous mixture that complicates detection and mitigation measures. However, most of the existing gas sensors are unsuitable for detecting such mixtures. In this study, we demonstrated the detection and identification of gaseous mixtures of TICs using a chemiresistor array of single-walled carbon nanotubes (SWCNTs). The array consists of three SWCNT chemiresistors coated with different molecular/ionic species, achieving a limit of detection (LOD) of 2.2 ppb for ammonia (NH3), 820 ppb for sulfur dioxide (SO2), and 2.4 ppm for ethylene oxide (EtO). By fitting the concentration-dependent sensor responses to an adsorption isotherm, we extracted parameters that characterize each analyte-coating combination, including the proportionality and equilibrium constants for adsorption. Principal component analysis confirmed that the sensor array detected and identified mixtures of two TIC gases: NH3/SO2, NH3/EtO, and SO2/EtO. Exposing the sensor array to three TIC mixtures with various EtO/SO2 ratios at a fixed NH3 concentration showed an excellent correlation between the sensor response and the mixture composition. Additionally, we proposed concentration ranges within which the sensor array can effectively detect the gaseous mixtures. Being highly sensitive and capable of analyzing both individual and mixed TICs, our gas sensor array has great potential for monitoring the safety and environmental effects of industrial chemical processes.

Application of Titanium Carbide MXenes in Chemiresistive Gas Sensors

The titanium carbide MXenes currently attract an extreme amount of interest from the material science community due to their promising functional properties arising from the two-dimensionality of these layered structures. In particular, the interaction between MXene and gaseous molecules, even at the physisorption level, yields a substantial shift in electrical parameters, which makes it possible to design gas sensors working at RT as a prerequisite to low-powered detection units. Herein, we consider to review such sensors, primarily based on Ti₃C₂T_x and Ti₂CT_x crystals as the most studied ones to date, delivering a chemiresistive type of signal. We analyze the ways reported in the literature to modify these 2D nanomaterials for (i) detecting various analyte gases, (ii) improving stability and sensitivity, (iii) reducing response/recovery times, and (iv) advancing a sensitivity to atmospheric humidity. The most powerful approach based on designing hetero-layers of MXenes with other crystals is discussed with regard to employing semiconductor metal oxides and chalcogenides, noble metal nanoparticles, carbon materials (graphene and nanotubes), and polymeric components. The current concepts on the detection mechanisms of MXenes and their hetero-composites are considered, and the background reasons for improving gas-sensing functionality in the hetero-composite when compared with pristine MXenes are classified. We formulate state-of-the-art advances and challenges in the field while proposing some possible solutions, in particular via employing a multisensor array paradigm.

Several ML Algorithms and Their Feature Vector Design for Gas Discrimination and Concentration Measurement with an Ultrasonically Catalyzed MOX Sensor

Although gas-borne ultrasound catalysis has been developed as a new method to discriminate gas species and measure the concentration, applications of machine learning methods in gas analyses with a single metal oxide (MOX) gas sensor catalyzed by gas-borne ultrasound are still scarce. In this work, with an ultrasonically catalyzed MOX gas sensor, we explored the effectiveness of K-nearest neighbors (KNN), support vector machine (SVM), and single-hidden-layer BP-ANN (SHBP) in gas discrimination and the application of the SHBP in concentration measurement. The target gases in this work are ethanol, acetone, methanol, hydrogen, and n-butane in clean air, respectively, and the discrimination and concentration regression are implemented by two different ML models. With the properly designed feature vectors, the SHBP method has an acceptable capability of both of species discrimination and concentration regression (success rate of gas discrimination = 99.5%, relative error of concentration regression = 6.406%). The KNN and SVM methods have similar capabilities of gas discrimination as the SHBP. This work also demonstrates a method to design the feature vectors for the ultrasonically catalyzed MOX gas sensor and to choose the feature parameters.

MXene: fundamentals to applications in electrochemical energy storage

A new, sizable family of 2D transition metal carbonitrides, carbides, and nitrides known as MXenes has attracted a lot of attention in recent years. This is because MXenes exhibit a variety of intriguing physical, chemical, mechanical, and electrochemical characteristics that are closely linked to the wide variety of their surface terminations and elemental compositions. Particularly, MXenes are readily converted into composites with materials including oxides, polymers, and CNTs, which makes it possible to modify their characteristics for a variety of uses. MXenes and MXene-based composites have demonstrated tremendous promise in environmental applications due to their excellent reducibility, conductivity, and biocompatibility, in addition to their well-known rise to prominence as electrode materials in the energy storage sector. The remarkable characteristics of 2D MXene, including high conductivity, high specific surface area, and enhanced hydrophilicity, account for the increasing prominence of its use in storage devices. In this review, we highlight the most recent developments in the use of MXenes and MXene-based composites for electrochemical energy storage while summarizing their synthesis and characteristics. Key attention is paid to applications in supercapacitors, batteries, and their flexible components. Future research challenges and perspectives are also described.

Application Prospects of MXenes Materials Modifications for Sensors

The first two-dimensional (2D) substance sparked a boom in research since this type of material showed potential promise for applications in field sensors. A class of 2D transition metal nitrides, carbides, and carbonitrides are referred to as MXenes. Following the 2011 synthesis of Ti₃C₂ from Ti₃AlC₂, much research has been published. Since these materials have several advantages over conventional 2D materials, they have been extensively researched, synthesized, and studied by many research organizations. To give readers a general understanding of these well-liked materials, this review examines the structures of MXenes, discusses various synthesis procedures, and analyzes physicochemistry properties, particularly optical, electronic, structural, and mechanical properties. The focus of this review is the analysis of modern advancements in the development of MXene-based sensors, including electrochemical sensors, gas sensors, biosensors, optical sensors, and wearable sensors. Finally, the opportunities and challenges for further study on the creation of MXenes-based sensors are discussed.

Portable and Label-Free Sensor Array for Discriminating Multiple Analytes via a Handheld Gas Pressure Meter

Cross-reactive sensor arrays are useful for discriminating multiple analytes in a complex sample. Herein, a portable and label-free gas pressure sensor array was proposed for multiplex analysis via a handheld gas pressure meter. It is based on the interaction diversity of analytes with catalase-like nanomaterials, including Pt nanoparticles (PtNP), Co₃O₄ nanosheets (Co₃O₄NS), and Pt-Co alloy nanosheets (PtCoNS), respectively. Thus, the diverse influence of analytes on the catalase-like activity could be output as the difference in the gas pressure. By using principal component analysis, eight proteins were well distinguished by the gas pressure sensor array at the 10 nM level within 12 min. Moreover, different concentrations of proteins and mixtures of proteins could likewise be discriminated. More importantly, the effective discrimination of proteins in human serum and discrimination of five kinds of cells further confirmed the potential of the gas pressure sensor array. Therefore, it provides a portable, cheap, sensitive, and label-free gas pressure sensor array, which is totally different from the reported sensor arrays and holds great potential for portable and cheap discrimination of multiple analytes.

Machine Learning-Assisted Multifunctional Environmental Sensing Based on a Piezoelectric Cantilever

Multifunctional environmental sensing is crucial for various applications in agriculture, pollution monitoring, and disease diagnosis. However, most of these sensing systems consist of multiple sensors, leading to significantly increased dimensions, energy consumption, and structural complexity. They also often suffer from signal interferences among multiple sensing elements. Herein, we report a multifunctional environmental sensor based on one single sensing element. A MoS₂ film was deposited on the surface of a piezoelectric microcantilever (300 × 1000 μm²) and used as both a sensing layer and top electrode to make full use of the changes in multiple properties of MoS₂ after its exposure to various environments. The proposed sensor has been demonstrated for humidity detection and achieved high resolution (0.3% RH), low hysteresis (5.6%), and fast response (1 s) and recovery (2.8 s). Based on the analysis of the magnitude spectra for transmission using machine learning algorithms, the sensor accurately quantifies temperatures and CO₂ concentrations in the interference of humidity with accuracies of 91.9 and 92.1%, respectively. Furthermore, the sensor has been successfully demonstrated for real-time detection of humidity and temperature or CO₂ concentrations for various applications, revealing its great potential in human-machine interactions and health monitoring of plants and human beings.

Rising of MXenes: Novel 2D-functionalized nanomaterials as a new milestone in corrosion science - a critical review

Corrosion is a natural process between a metal and its environment that can gradually cause catastrophic damage to the metal equipment, which would have economic implications. Consequently, several protective methods have been utilized to prevent metals from severe degradation. Organic polymeric coatings have been widely used as the most convenient and cost-effective method to boost metals' anti-corrosion properties. Nonetheless, these coatings have a significant amount of solvent, resulting in shrinkage and micro defects in the films during the curing process. Many studies have verified that transition metal carbides/nitrides (MXenes) can form a "labyrinth effect" in the polymeric coatings due to their "nano-barrier effect". Furthermore, based on their sheet-like structures, they can considerably cover the surface defects of the polymeric films. Therefore, the penetration of corrosive elements can be substantially curbed. It is the first review that specifically focused on the new family of 2D nanomaterials, i.e., MXenes, and discussed their applications in corrosion protection systems. The MXenes' pros and cons in the polymeric matrixes as nanofillers will be clarified. Moreover, the synthesis and functionalization methods of the MXenes, their applications, and corrosion protection mechanism will be explored. Subsequently, the MXenes' superiority over other 2D nanomaterials will be highlighted while their future perspectives and industrial applications will be predicted.

Fully Inkjet-Printed Chemiresistive Sensor Array Based on Molecularly Imprinted Sol-Gel Active Materials

The fabrication of chemiresistive sensors by inkjet printing is recognized as a breakthrough in gas-sensing applications. One challenge of this technology, however, is how to enhance the cross-selectivity of the sensor array. Herein, we present a ketjen black (KB) ink and molecularly imprinted sol-gel (MISG) inks to support the fabrication of a fully inkjet-printed chemiresistive sensor array, enabling the highly accurate recognition of volatile organic acids (VOAs) on the molecular level. The MISG/KB sensor array was prepared on a glossy photographic paper with a three-layer structure: a circuit layer by a commercial silver ink, a conductive layer by a KB ink, and an active selective layer by MISG inks imprinted by different templates. Hexanoic acid (HA), heptanoic acid, and octanoic acid were used as templates to prepare the MISGs and as targets to evaluate the detection and discrimination performance of the sensor array. Three resultant MISG/KB sensors exhibited high sensitivity and selectivity to VOA vapors. The limit of detection and imprinting factor were 0.018 ppm and 7.82, respectively, for HA-MISG/KB sensors to the corresponding target. With linear discriminant analysis of the gas responses, the MISG/KB sensor array can realize high discrimination to VOAs in single and binary mixtures. Furthermore, the proposed sensor array showed strong sensor robustness with excellent consistency, durability, bending, and humidity resistance. This work developed a fully inkjet-printed chemiresistive sensor array, enabling the realization of high cross-selectivity detection, achieving low-cost, scalable, and highly reproducible sensor fabrication, moving it closer to reliable, commercial, and wearable multi-analyte human body odor analysis potential.

Chemical Discrimination of Benzene Series and Molecular Recognition of the Sensing Process over Ti-Doped Co3O4

This work achieved the chemical discrimination of benzene series (toluene, xylene isomers, and ethylbenzene gases) based on the Ti-doped Co₃O₄ sensor. Benzene series gases presented different gas-response features due to the differences in redox rate on the surface of the Ti-doped Co₃O₄ sensor, which created an opportunity to discriminate benzene series via the algorithm analysis. Excellent groupings were obtained via the principal component analysis. High prediction accuracies were acquired via k-nearest neighbors, linear discrimination analysis (LDA), and support vector machine classifiers. With the confusion matrix for the data set using the LDA classifier, the benzene series have been well classified with 100% accuracy. Furthermore, in situ diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) and density functional theory calculations were conducted to investigate the molecular gas-solid interfacial sensing mechanism. Ti-doped Co₃O₄ showed strong Lewis acid sites and adsorption capability toward reaction species, which benefited the toluene gas-sensing reaction and resulted in the highly boosted gas-sensing performance. Our research proposed a facile distinction methodology to recognize similar gases and provided new insights into the recognition of gas-solid interfacial sensing mechanisms.

Virtual Sensor Array Based on Piezoelectric Cantilever Resonator for Identification of Volatile Organic Compounds

Piezoelectric cantilever resonator is one of the most promising platforms for real-time sensing of volatile organic compounds (VOCs). However, it has been a great challenge to eliminate the cross-sensitivity of various VOCs for these cantilever-based VOC sensors. Herein, a virtual sensor array (VSA) is proposed on the basis of a sensing layer of GO film deposited onto an AlN piezoelectric cantilever with five groups of top electrodes for identification of various VOCs. Different groups of top electrodes are applied to obtain high amplitudes of multiple resonance peaks for the cantilever, thus achieving low limits of detection (LODs) to VOCs. Frequency shifts of multiple resonant modes and changes of impedance values are taken as the responses of the proposed VSA to VOCs, and these multidimensional responses generate a unique fingerprint for each VOC. On the basis of machine learning algorithms, the proposed VSA can accurately identify different types of VOCs and mixtures with accuracies of 95.8 and 87.5%, respectively. Furthermore, the VSA has successfully been applied to identify the emissions from healthy plants and "plants with late blight" with an accuracy of 89%. The high levels of identifications show great potentials of the VSA for diagnosis of infectious plant diseases by detecting VOC biomarkers.

Oxygen-Vacancy-Induced Synaptic Plasticity in an Electrospun InGdO Nanofiber Transistor for a Gas Sensory System with a Learning Function

The perceptual learning function of a simulating human body is very important for constructing a neural computing system and a brainlike computer in the future. The sense of smell is an important part of the human sensory nervous system. However, current gas sensors simply convert gas concentrations into electrical signals and do not have the same learning and memory function as synapses. To solve this problem, we propose a new sensing idea to induce and activate the synaptic properties of transistors by adjusting the oxygen vacancy in the active layer. This sensor combines gas detection with synaptic memory and learning and overcomes the disadvantage of the separation of synaptic transistors and sensors, thus greatly reducing the cost of production. This work combines the detection of N,N-dimethylformamide (DMF) gas with the synaptic mechanism of human olfactory nerves. We successfully fabricated an InGdO nanofiber field-effect transistor by electrostatic spinning and simulated the response of human olfactory synapses to target gas by regulating the oxygen vacancy of the InGdO nanofiber. The synaptic transistor response under different concentrations of unmodulated pulses is tested, and the pavlovian conditioned reflex experiment is simulated successfully. This work provides a new idea of a gas sensor device, which is very important for the development of high-performance gas sensors and bionic electronic devices in the future.

Machine learning-assisted array from fluorescent conjugated microporous polymers for multiple explosives recognition

The fluorescent properties of conjugated microporous polyphenylene (CMPs) were tuned through a wide range by inclusion of small amount of comonomer as chromophore in the network. The multi-color CMPs were used for explosives sensing and demonstrated broad sensitivity (ranging from -0.01888 μM^-1 to -0.00467 μM^-1) and LODs (ranging from 31.0 nM to 125.3 nM) against thirteen explosive compounds including nitroaromatics (NACs), nitramines (NAMs) and nitrogen-rich heterocycles (NRHCs). The CMPs were also developed as a sensor array for discrimination of thirteen explosives, specifically including NT, p-DNB, DNT, TNT, TNP, TNR, RDX, HMX, CL-20, FOX-7, NTO, DABT and DHT. By using classical statistical method "Linear Discriminant Analysis (LDA)", the thirteen explosives at a fixed concentration were completely discriminated and unknown test samples were indentied with 88% classification accuracy. Moreover, explosives in different concentrations and the mixtures of explosives were also successfully classified. Compared with LDA, Machine Learning algorithms have significant advantages in analyzing the array-based sensing data. Different Machine Learning models for pattern recognition have also been implemented and discussed here and much higher accuracy (96% for "neural network") can be achieved in predicting unknown test samples after training.

Characterizing the Chemical Structure of Ti3C2Tx MXene by Angle-Resolved XPS Combined with Argon Ion Etching

Angle-resolved XPS combined with argon ion etching was used to characterize the surface functional groups and the chemical structure of Ti3C2Tx MXene. Survey scanning obtained on the sample surface showed that the sample mainly contains C, O, Ti and F elements, and a little Al element. Analyzing the angle-resolved narrow scanning of these elements indicated that a layer of C and O atoms was adsorbed on the top surface of the sample, and there were many O or F related Ti bonds except Ti-C bond. XPS results obtained after argon ion etching indicated staggered distribution between C-Ti-C bond and O-Ti-C, F-Ti bond. It is confirmed that Ti atoms and C atoms were at the center layer of Ti3C2Tx MXene, while O atoms and F atoms were located at both the upper and lower surface of Ti3C2 layer acting as surface functional groups. The surface functional groups on the Ti3C2 layer were determined to include O2-, OH-, F- and O--F-, among which F atoms could also desorb from Ti3C2Tx MXene easily. The schematic atomic structure of Ti3C2Tx MXene was derived from the analysis of XPS results, being consistent with theoretical chemical structure and other experimental reports. The results showed that angle-resolved XPS combing with argon ion etching is a good way to analysis 2D thin layer materials.

Prospects and Challenges of MXenes as Emerging Sensing Materials for Flexible and Wearable Breath-Based Biomarker Diagnosis

A fully integrated, flexible, and functional sensing device for exhaled breath analysis drastically transforms conventional medical diagnosis to non-invasive, low-cost, real-time, and personalized health care. 2D materials based on MXenes offer multiple advantages for accurately detecting various breath biomarkers compared to conventional semiconducting oxides. High surface sensitivity, large surface-to-weight ratio, room temperature detection, and easy-to-assemble structures are vital parameters for such sensing devices in which MXenes have demonstrated all these properties both experimentally and theoretically. So far, MXenes-based flexible sensor is successfully fabricated at a lab-scale and is predicted to be translated into clinical practice within the next few years. This review presents a potential application of MXenes as emerging materials for flexible and wearable sensor devices. The biomarkers from exhaled breath are described first, with emphasis on metabolic processes and diseases indicated by abnormal biomarkers. Then, biomarkers sensing performances provided by MXenes families and the enhancement strategies are discussed. The method of fabrications toward MXenes integration into various flexible substrates is summarized. Finally, the fundamental challenges and prospects, including portable integration with Internet-of-Thing (IoT) and Artificial Intelligence (AI), are addressed to realize marketization.

A flexible virtual sensor array based on laser-induced graphene and MXene for detecting volatile organic compounds in human breath

Detecting volatile organic compounds (VOCs) in human breath is critical for the early diagnosis of diseases. Good selectivity of VOC sensors is crucial for the accurate analysis of VOC biomarkers in human breath, which consists of more than 200 types of VOCs. In this paper, a flexible virtual sensor array (FVSA) was proposed based on a sensing layer of MXene and laser-induced graphene interdigital electrodes (LIG-IDEs) for detecting VOCs in exhaled human breath. The fabrication of LIG-IDEs avoids the costly and complicated procedures required for the preparation of traditional IDEs. The FVSA's responses of multiple parameters help build a unique fingerprint for each VOC, without a need for changing the temperature of the sensing element, which is commonly used in the VSA of semiconductor VOC sensors. Based on machine learning algorithms, we have achieved highly precise recognition of different VOCs and mixtures and accurate prediction (accuracy of 89.1%) of the objective VOC's concentration in variable backgrounds using this proposed FVSA. Moreover, a blind analysis validates the capacity of the FVSA to identify alcohol content in human breath with an accuracy of 88.9% using breath samples from volunteers before and after alcohol consumption. These results show that the proposed FVSA is promising for the detection of VOC biomarkers in human exhaled breath and early diagnosis of diseases.

Preparation and Application of 2D MXene-Based Gas Sensors: A Review

Since MXene (a two-dimensional material) was discovered in 2011, it has been favored in all aspects due to its rich surface functional groups, large specific surface area, high conductivity, large porosity, rich organic bonds, and high hydrophilicity. In this paper, the preparation of MXene is introduced first. HF etching was the first etching method for MXene; however, HF is corrosive, resulting in the development of the in situ HF method (fluoride + HCl). Due to the harmful effects of fluorine terminal on the performance of MXene, a fluorine-free preparation method was developed. The increase in interlayer spacing brought about by adding an intercalator can affect MXene’s performance. The usual preparation methods render MXene inevitably agglomerate and the resulting yields are insufficient. Many new preparation methods were researched in order to solve the problems of agglomeration and yield. Secondly, the application of MXene-based materials in gas sensors was discussed. MXene is often regarded as a flexible gas sensor, and the detection of ppb-level acetone at room temperature was observed for the first time. After the formation of composite materials, the increasing interlayer spacing and the specific surface area increased the number of active sites of gas adsorption and the gas sensitivity performance improved. Moreover, this paper discusses the gas-sensing mechanism of MXene. The gas-sensing mechanism of metallic MXene is affected by the expansion of the lamellae and will be doped with H2O and oxygen during the etching process in order to become a p-type semiconductor. A p-n heterojunction and a Schottky barrier forms due to combinations with other semiconductors; thus, the gas sensitivities of composite materials are regulated and controlled by them. Although there are only several reports on the application of MXene materials to gas sensors, MXene and its composite materials are expected to become materials that can effectively detect gases at room temperature, especially for the detection of NH3 and VOC gas. Finally, the challenges and opportunities of MXene as a gas sensor are discussed.

Biocompatible MXene (Ti3C2Tx) Immobilized with Flavin Adenine Dinucleotide as an Electrochemical Transducer for Hydrogen Peroxide Detection in Ovarian Cancer Cell Lines

Flavin adenine dinucleotide (FAD) is a coenzyme and acts as a redox cofactor in metabolic process. Owing to such problems as poor electron transfer properties, unfavorable adsorption, and lack of stability on rigid electrodes, the bio-electrochemical applications of FAD have been limited. Herein, a novel fabrication method was developed for the immobilization process using 2D MXene (Ti₃C₂T_x), which enhanced the redox property of FAD and improved the electro-catalytic reduction of hydrogen peroxide (H₂O₂) in neutral medium. The FAD-immobilized Ti₃C₂T_x electrode (FAD/Ti₃C₂T_x) was studied by UV-Visible and Raman spectroscopies, which confirmed the successful adsorption of FAD on the Ti₃C₂T_x surface. The surface morphology and the elemental composition of Ti₃C₂T_x were investigated by high resolution transmission electron microscopy and the energy dispersive X-ray analysis. The redox property of the FAD/Ti₃C₂T_x modified glassy carbon electrode (FAD/Ti₃C₂T_x/GCE) was highly dependent on pH and exhibited a stable redox peak at −0.455 V in neutral medium. Higher amounts of FAD molecules were loaded onto the 2D MXene (Ti₃C₂T_x)-modified electrode, which was two times higher than the values in the reported work, and the surface coverage (ᴦ_FAD) was 0.8 × 10⁻¹⁰ mol/cm². The FAD/Ti₃C₂T_x modified sensor showed the electrocatalytic reduction of H₂O₂ at −0.47 V, which was 130 mV lower than the bare electrode. The FAD/Ti₃C₂T_x/GCE sensor showed a linear detection of H₂O₂ from 5 nM to 2 µM. The optimization of FAD deposition, amount of Ti₃C₂T_x loading, effect of pH and the interference study with common biochemicals such as glucose, lactose, dopamine (DA), potassium chloride (KCl), ascorbic acid (AA), amino acids, uric acid (UA), oxalic acid (OA), sodium chloride (NaCl) and acetaminophen (PA) have been carried out. The FAD/Ti₃C₂T_x/GCE showed high selectivity and reproducibility. Finally, the FAD/Ti₃C₂T_x modified electrode was successfully applied to detect H₂O₂ in ovarian cancer cell lines.

Fabrication of a Ti3C2Tx modified glassy carbon electrode for the sensitive electrochemical detection of quercetin

Fabrication of MXene/GCE for the electrochemical determination of quercetin.