Evanescent-wave gas sensors employing side-polished optical fibers (SPFs) functionalized with nanomaterial coatings represent a promising platform for compact, sensitive detection. While single-walled carbon nanotube (SWCNT) films are recognized for their gas adsorption capabilities, their integration with photonic structures often overlooks complex light-matter interactions. In this work, we report a counterintuitive polarization-dependent response in an evanescent-wave NO2 sensor, fabricated by depositing aerosol-synthesized SWCNT thin films onto SPFs. The device demonstrates high performance, including a limit of detection of 400 ppb and stable operation in humid environments. However, its sensing behavior deviates strikingly from established models: upon NO2 exposure, transmitted light intensity increases for TM polarization but decreases for TE polarization, a phenomenon not attributable solely to changes in the intrinsic absorption of the SWCNTs. We pinpoint that the dominant mechanism is a gas-induced alteration of the SWCNT film's complex refractive index, which subsequently perturbs the evanescent field mode profile of the waveguide. Numerical simulations confirm that accounting for this mode-profile redistribution is essential to accurately describe the sensor's response. Revealed mechanism provides an important design framework for advanced evanescent-field sensors based on tunable nanomaterial claddings.

Halide perovskite single crystals offer a promising platform for gas sensing application owing to their structural and optoelectrical properties, which enable high sensitivity toward various gaseous analytes. However, the discrimination of various gases within one device remains challenging. This study proposes a design of optoelectronic gas sensor based on CsPbBr3 perovskite single microcrystal for the detection of NO2 and NH3 in the mixture with air at room temperature. Tunable light intensity excitation affords fast response time of 2 s and high sensitivity with detection limit reaching 0.1 and 0.24 ppm for NO2 and NH3 in the mixture with air, respectively. In combination with machine-learning algorithms, the exact type of analyte is identified with 96% accuracy using a Random Forest Classifier. Additionally, using a regression model derived from the Langmuir adsorption equation, the exact concentration of gas was estimated with mean absolute percentage errors in the range of 20.1%–43.9%. The reported results establish single-crystal perovskite gas sensors as promising devices for real-time environmental monitoring.

This work systematically investigates how thermal, chemical and mechanical post-fabrication treatments influence the electrical performance of wet-pulled carbon nanotube fibers (CNTFs). The induced changes caused by combining the treatments were evaluated using Raman and Fourier Transform Infrared spectroscopy, scanning electron and atomic force microscopy (SEM and AFM), and DC electrical characterization techniques. Typical wet-pulled CNTFs showed electrical conductivity values of similar to 600 S/cm, and thermal treatment slightly reduced the conductivity to similar to 450 S/cm. This decrease is compensated by the increase in susceptibility towards doping with HAuCl4, yielding a conductivity increase up to similar to 1400 S/cm. When thermal annealing and mechanical densification are followed by immersion in HAuCl4 solution, electrical conductivity as high similar to 10,000 S/cm can be reached due to the surface deposition of gold particles. However, by combining doping and mechanical densification treatments simultaneously, the precipitation and distribution of gold particles can be homogeneous throughout the volume of the CNTFs, leading to similar conductivity (similar to 9000 S/cm) with better resilience under applied voltage. This work aims to clearly outline the relationship between the structural and electrical changes caused by each augmentation technique, as well as their combinations, allowing rational design selection for high performance CNTF alternative conductors.

Functional hybrid nanomaterials on the basis of reduced graphene oxide (RGO) and metal-containing nano-particles (NPs) are of increasing interest due to their tunable physical and chemical properties. In this study, we comparatively evaluate a simultaneus synthesis of hybrides through thermal reduction and supercritical isopropanol (SCI) treatment utilizing nickel acetylacetonate as a metal precursor and graphene oxide to form metal-based nanoparticles on the surface of RGO. SCI treatement yields the formation of metallic nickel NPs with an average diameter of 11 nm on the RGO surface, whereas thermal reduction facilitate appearance of nickel oxide (NiO) NPs with an average size of 5 nm on the surface of partially reduced graphene oxide. Hybrid materials produced by treatment in SCI exhibits good conductive and magnetic properties. Both surface resistance and saturation magnetization (Ms) strongly depend on the content of metal NPs in the hybrid. The maximum Ms value for RGO hybrids syntesized by SCI was 13.9 emu/g. These materials hold potential for flexible electronics, piezomagnetic sensors, and catalysis applications.

This study demonstrates a robust approach for producing Ni and Au nanowires (NWs) encapsulated by single-walled carbon nanotubes (SWCNTs), achieving lengths up to 1.2 µm. The process involves nitrogen plasma treatment to create defects in the SWCNTs, followed by the nanotubes filling with the metal precursor and its subsequent reduction. Simulations of carbon nanotube irradiation are performed to investigate the relationship between nitrogen kinetic energy and defect formation under the irradiation, revealing conditions that promote efficient nickel encapsulation. Structural analysis confirmed defect sizes ranging from 3.6 to 9.2 Å, suitable for metal precursor entry. Ni NWs exhibited excellent electrocatalytic activity in urea oxidation reactions (UOR), achieving a specific activity of 1150 A g-1 at 1.7 V vs. reversible hydrogen electrode (RHE) in 2 M urea and stable performance over 1000 cycles. Comparative simulations of urea adsorption energies showed that nickel-filled SWCNTs enhance adsorption (-0.35 eV) when compared to pure graphene surface (-0.2 eV), demonstrating a synergistic effect of nickel and carbon structures. The role of defects in enhancing urea adsorption has also been analyzed. This work highlights the potential of SWCNTs as nano-reactors for producing high-performance catalytic materials. The findings emphasize the importance of controlled defect engineering and thermal treatment in optimizing nanowire synthesis for advanced catalytic and functional applications.

Carbon nanotube fibers (CNTFs) fabricated using a novel “wet-pulling” technique were evaluated for electrochemical detection of dopamine (DA). To enhance the sensitivity, we employed an electrochemical oxidation pretreatment and twisting of CNTFs and investigated the performance by cyclic voltammetry (CV) and chronoamperometry (CA). The synergetic effect of twisting and pre-oxidation boosted the sensitivity of modified CNTFs compared to the as-obtained CNTFs due to the introduction of new active sites for efficient trapping of DA molecules. For the oxidized twisted CNTFs, we achieved a sensitivity of 10.8 µA·µM–1·cm–2 and the limit of detection of 330 nM in the case of CV, and 6.4 µA·µM−1·cm−2 and 102 nM in the case of CA, respectively. The CNTFs showed good potential for an in vitro application, maintaining sufficient sensitivity under various conditions. When coupled with the suited mechanical properties of CNTFs, the results highlight the prospect of implantable and flexible sensors.

Background The multisensor concept has been developed as a powerful alternative to well-known gas-analytical instrumentation for applications where a fast but accurate and reliable assessment of the environment is required. The concept follows a biology-inspired approach where the selectivity towards various gases/odors is attained via pattern recognition of multisensory signal vectors. Herein, we discuss how to design a selective multisensor library based on various metal oxide nanostructures like a lab-on-chip using a simple but efficient bottom-up growth of materials over the multi-electrode chip under robust dc electrochemical protocols. Results In addition to a conventional growth of oxide layers over the metal electrodes, we show that the fine nanowall-like oxide structures appear as a quasi-matrixed percolation film over the SiO2 substrate surface in the inter-electrode gaps to constitute a chemiresistive film. We have tested two directions while applying the technique to grow Co, Ni, Mn, and Zn oxides to develop on-chip sensor arrays of, (i) monoxide type employing the oxide films with gradual change of growth time, and (ii) multi-oxide type based on the four oxides. The materials were thoroughly characterized by electron microscopy, X-ray diffraction, thermogravimetric analysis, and X-ray photoelectron spectroscopy/mapping to prove the composition and structure. Among tested oxides, ZnO readily appears not only at the electric potential-targeted chip zone but also in other areas to dope the films for yielding heterojunctions with other oxides that enhances a variability of functional properties in the on-chip sensor array. The gas-sensing performance of the chips has been tested versus various chemically akin alcohol vapors at the sub- and low ppm range of concentrations in a mixture with air. Significance We show that the grown oxide nanostructures exhibit a high-sensitive chemiresistive signal which allows one to build a multisensor vector signal, selective to the kind of alcohols, even at sub-ppm concentrations. Moreover, the multi-oxide library yields options for a superior selectivity under LDA metrics than the gradient-grown mono-oxide one due to the versatility of materials while the low-cost growth protocols remain to be the same in both cases. The delivered method to produce multisensor arrays allows one producing low-cost but efficient electronic nose units for numerous applications.

Structural defects and heteroatoms play a key role in electrochemical reactions. However, there is still no common understanding of what has a greater impact on electrochemical processes: defects or the type of heteroatoms. To clarify these factors, defective carbon nanowalls treated by reactive etching in different atmospheres, such as argon and mixtures of argon with nitrogen, chlorine, hydrogen bromide, and sulfur fluoride were used. Properties of the obtained samples were analyzed with Raman spectroscopy, X-ray photoelectron spectroscopy, scanning electron microscopy, and cyclic voltammetry. The results of the study showed that the plasma modification of carbon nanowalls leads to the removal of the amorphous layer and subsequent implantation of heteroatoms, which ultimately leads to an increase in their areal capacitance 1.5-fold in a 1:2 argon - nitrogen mixture and 2-fold in a 1:4 argon-nitrogen mixture.

Monitoring toxic volatile sulfur compounds (VSCs) by metal-oxide-semiconductor (MOS) sensors has gained much attention for various applications including smart factory and health screening. Although room temperature (RT) operation is highly preferred due to tiny power consumption and minimal explosion risk of VSCs, poor selectivity and insufficient molecule features acquired at RT pose a big challenge. Herein, a 16-channel MOS-based electronic nose (e-nose) has been integrated in a glass wafer with ITO interdigital electrode arrays, visible light modulation, and three kinds of feature extraction methods have been proposed to extract the (subtle) features of those VSCs molecules. Combining the transient e-nose response characteristics generated by visible light modulation and convolutional neural network (CNN) algorithm, a high prediction accuracy of 99.2% toward five kinds of VSCs with varying concentrations could be achieved. Furthermore, SHapley Additive exPlanations (SHAP) approach has been used to estimate the contribution of individual sensors in prediction models for the optimization of model computational complexity and sensor array size. This work sheds light on the rational screening of useful sensors for constructing high-performance e-nose with minimal costs for various applications.

Hierarchically reinforced multifunctional nanocomposites are leading-edge advanced materials. Single-walled carbon nanotubes (SWCNT) are one of their prime additives, exacerbating functional property development at much lower addition than other carbon allotropes. Here, we prove that SWCNT addition at percolation threshold amounts can provide multifunctional performance regardless of their quality. A plasma etching technique was used to induce SWCNT defectiveness (I-G/I-D ratio drops from 66 to 23). These were used to produce SWCNT/carbon fiber/thermoset nanocomposites with concentrations near percolation levels (0.005 wt%). Multifunctional characterization showed that differences in performance were virtually non-existent. For nanocomposites with additives of different quality, ultimate tensile strength varied between 630 and 645 MPa and flexural strength between 560 and 640 MPa. Both tensile and flexural moduli were within 10% variance. In- and through-plane functional properties, at room and elevated temperature, were also effectively identical. Electrical and thermal conductivity measured up to 150 S cm(-1) and 3.7 W(m K)(-1), respectively, while thermal capacity and diffusivity were as high as 1.2 J(g K)(-1) and 2.7 mm(2) s(-1). All nanocomposites were thermally stable till similar to 300 degrees C and microstructural analysis showed no SWCNT-defect connected effects. Thus, SWCNTs may allow relaxation in typical quality parameters during large-scale production, reducing current testing and control requirements and thereby, production costs.

Filled single-walled carbon nanotubes (f-SWCNT) are regarded as prospective materials with diverse applications, owing to the superior combination of their intrinsic properties with those of the filler material. The variety of the potential filler materials makes solution filling through open SWCNT channels under capillary forces the most flexible method for f-SWCNT production. However, the technique requires the additional step of SWCNT cap removal, and remains poorly efficient. Here, we evaluated how the structure of the SWCNTs impacts the filling efficiency depending on the conditions of electrochemical opening treatment under cyclic voltammetry protocols. We revealed the optimal upper vertex potential to be 1.1 V vs. RHE, corresponding to the trade-off between end-cap removal efficiency and yield of wall defect formation. It allowed us to achieve a filling efficiency of ∼1680 m cm−2 of gold nanowires length per unit SWCNT film area, which is ∼10 times greater than the filling efficiency of the untreated material.

Cathodic electroactive bacteria (C-EAB) which are capable of accepting electrons from solid electrodes provide fresh avenues for pollutant removal, biosensor design, and electrosynthesis. This review systematically summarized the burgeoning applications of the C-EAB over the past decade, including 1) removal of nitrate, aromatic derivatives, and metal ions; 2) biosensing based on biocathode; 3) electrosynthesis of CH4, H2, organic carbon, NH3, and protein. In addition, the mechanisms of electron transfer by the C-EAB are also classified and summarized. Extracellular electron transfer and interspecies electron transfer have been introduced, and the electron transport mechanism of typical C-EAB, such as Shewanella oneidensis MR-1, has been combed in detail. By bringing to light this cutting-edge area of the C-EAB, this review aims to stimulate more interest and research on not only exploring great potential applications of these electron-accepting bacteria, but also developing steady and scalable processes harnessing biocathodes.

The widespread adoption of e-nose devices based on chemiresistive materials has been hindered by issues related to sensor device complexity and reliability, specifically sensor drift, necessitating frequent recalibration and retraining of pattern recognition models. This study introduces a method for thermocycling a single sensor based on a free-standing network of single-walled carbon nanotubes (SWCNTs) to acquire signal patterns for selective analyte detection. Additionally, it employs a data filtering technique to compensate for the sensor drift. A freestanding SWCNT film, only a few nanometers thick, is thermally cycled via Joule heating between room temperature and 120 degrees C. Under these conditions, the sensitivity was tested towards NO2, H2S, and acetone vapors (10-25 ppm) in the mixture with dry air. Signal patterns produced through thermocycling were processed using CatBoost and LSTM algorithms. The accuracy of detection reached 90 % in the classification task, and the average root mean squared error of analyte concentration detection in the multioutput regression task was below 4 ppm. By combining original sensor design, thermocycling, signal filtering for drift compensation, and advanced pattern recognition models, this work contributes to overcoming the challenges in multivariate sensing systems, paving the way for practical applications of the more reliable chemiresistive sensors.

Diamond's unique properties make it attractive for use in a variety of industrial applications. However, this material has not found mass application in microelectronics due to several factors, including the lack of largesized plates, n-type doping, and high-quality metallization. In this article, we address the problem of diamond surface metallization by forming niobium carbide layers. We obtained a niobium carbide film several nanometers thick that exhibits superconducting behavior up to 12.4 K. To our knowledge, this is the highest superconducting transition temperature achieved in the niobium carbide system. The crystal lattice parameter of the film is 4.4659 angstrom, which is close to the maximum value for niobium carbide lattice parameters. Density functional theory calculations were employed to investigate the thermodynamic stability of niobium carbide compounds at various temperatures and determine the superconducting critical temperature of niobium carbide. The combination of diamond's high thermal conductivity, along with the strong adhesion and superconductivity of niobium cabide films, introduces exciting possibilities for the realization of superconductive sensitive detectors.

Catalytic valveless micropumps, and membraneless fuel cells are the class of devices that utilize the decomposition of hydrogen peroxide (H2O2) into water and oxygen. Nonetheless, a significant obstacle that endures within the discipline pertains to the pragmatic open circuit potential (OCP) of hydrogen peroxide FCs (H2O2 FCs), which fails to meet the theoretical OCP. Additionally, bubble formation significantly contributes to this disparity, as it disrupts the electrolyte's uniformity and interferes with reaction dynamics. In addition, issues such as catalyst degradation and poor kinetics can impact the overall cell efficiency. The development of high-performance H2O2-FCs necessitates the incorporation of selective electrocatalysts with a high surface area. However, porous micro-structures of the electrode impedes the transport of fuel and the removal of reaction byproducts, thereby hindering the attainment of technologically significant rates. To address these challenges, including bubble formation, the review highlights the potential of integrating electrokinetic and bubble-driven micropumps. An alternative approach involves the spatiotemporal separation of fuel and oxidizer through the use of laminar flow-based fuel cell (LFFC). The present review addresses multifaceted challenges of H2O2-powered FCs, and proposes integration of electrokinetic and bubble-driven micropumps, emphasizing the critical role of bubble management in improving H2O2 FC performance. This review examines hydrogen peroxide-powered valveless micropumps and membraneless Fuel Cells (H2O2 FCs). It offers an in-depth analysis of materials design principles, operational mechanisms, and potential for synergistic integration, highlighting the prospects of innovative devices in the forefront of sustainable energy solutions. image

Carbon nanotube fibers (CNTFs) are a promising economical replacement material for contemporary metallic wired conductors due to their lightweight and advanced electromechanical properties. The wet‐pulling technique for CNTF manufacturing is highly versatile, adaptable for both laboratory as well as industrial‐scale production, and can be optimized for the maximum enhancement of electrophysical properties. Herein, a mechanosolvent‐based postfabrication approach for maximizing densification and improving electrical properties of wet‐pulled CNTFs is examined. The experimental process results in fibers achieving 60% of the theoretically maximum density and conductivity of maximally densified metallic single‐walled carbon nanotube bundles. The technique allows a corresponding increase of ≈700% in fiber density (from 100 to 704 kg m−3), a simultaneous ≈530% increase in electrical conductivity (from 748 to 3990 S cm−1), and reduced volume defects from 18% to 2%. The approach was combined with a step‐wise microstructure monitoring using focused ion beam–scanning electron microscopy to determine the mechanisms behind the optimized structures. This work is the first to provide an experimental and theoretical base for the postfabrication optimization of wet‐pulled CNTFs and lays the foundation for further enhancement with techniques such as chemical doping, fiber compounding, and combined infiltration/densification mechanisms.

Understanding the composition of gas mixtures is still a primary prerogative of complex analytical units or biological olfaction systems. Attempts to mimic the olfactory processes by using a multisensor array combined with machine learning algorithms led mainly to solving a problem of a selective classification of odors or regression over the range of concentrations of the same odor. The identification of individual analytes in a mixture remains a difficult task. In this study, we test the identification of individual chemicals in the composition of the gas mixture with a feature extraction algorithm using a multisensor array based on aluminum-doped zinc oxide. Our approach is based on matching the selected parameters of the response curves which share considerable similarity if a volatile compound is common in any two mixture combinations. We demonstrate the efficiency of the method by analyzing five analytes such as acetone, benzene, methanol, ethanol, and isopropanol, and their mixtures. As a result, we were able to efficiently classify all 31 odors with an accuracy of about 99%. We have achieved the mean values of F1 scores of 0.52, 0.63, and 0.59 reaching up to 0.80-0.86 for the prediction of every individual analyte in 2-, 3- and 4-component gas mixtures, respectively. While using just raw signals at steady state, we found that the results become rather biased as the number of analytes increases in a mixture. Thus, our approach enables an improved, more accurate, and thorough examination of the gas mixtures, expanding the scope of application of multisensor systems beyond the common "classification" tasks.

In our study, we leveraged an electronic nose to detect the patterns of crude oils and their mixtures, sourced from the oil fields from neighboring regions in pursuit of the task of environmental impact evaluation. The temporal dynamics of oil-related patterns acquired by an electronic nose was tracked to identify the influence of high or low emissions of volatiles that depend on the oil composition. Analyzing the oils by Fourier-transform IR-spectroscopy and GC×GC-MS, we confirmed the correlation between sensor responses and the oil compositions, significantly dependent on the ratio of aromatic compounds/alkanes. Using pattern recognition techniques, Random Forest classifier enabled good accuracy of classification of oil samples and contaminated soils underscoring a high-resolution distinction between the response data. Applying these principles to determine the oil origin, we observed that the studied oil samples and contaminated soil samples corroborate with the dynamic changes in odor patterns based only on volatile and semivolatile compounds. Crude oils from the border of two oil fields facilitate a change in the odor pattern to remain one of the fields depending on the weathering time. These proposed intelligent multisensor systems show great promise as a tool for estimating oil-contaminated soils, thereby potentially enhancing environmental monitoring practices.

Carbon materials are of outstanding interest for use in energy sources. One of the latest achievements in improving their specific characteristics is the doping of carbon materials with various heteroatoms. However, the mechanisms that lead to improved performance remain unexplored. In this study, we investigated the influence of structural defects and incorporated heteroatoms on the oxygen reduction reaction (ORR) of highly oriented pyrolytic graphite and carbon nanowalls. Controllable modification in DC plasma in an atmosphere of nitrogen, oxygen and air was used for the incorporation of heteroatoms. We found that treatment in the air atmosphere leads to the formation of most active sites due to incorporation of heteroatoms and partial amorphization of material surface. The DFT calculations reveal these active sites can't be amplified by substitution to nitrogen atom due to insignificant sorption energy difference of *OOH group compared with undoped carbon. Existed material's structural defects and appeared after the treatment also make a significant contribution to the obtained ORR characteristics. Plasma-assisted treatment under air conditions can be used for carbon nanomaterials modification for ORR application.