Intelligent MEMS Sensor Based on an Oxidized Medium-Entropy Alloy (FeCoNi) for H2 and CO Recognition

In this paper, we present the design and evaluation of an intelligent MEMS sensor employing the oxidized medium-entropy alloy (O-MEA) of FeCoNi as the gas-sensing material. Due to the specific catalytic exothermic reaction of the O-MEA on H2/CO, the sensor shows great selectivity for H2 and CO at 150 °C of the integrated microheater in the MEMS device, with the theoretical detection limit of 0.3 ppm for H2 and 0.29 ppm for CO. The MEMS heater, capable of instantaneous temperature changes in pulse operation mode, offers a novel approach for thermal modulation of the sensor, which is crucial for the adsorption and reaction of H2/CO molecules on the sensing layer surface. Consequently, we investigate the gas-sensing capabilities of the sensor under pulse heating modes (PHMs) of the MEMS heater and then propose the gas-sensing mechanism. The results indicate that PHMs significantly not only reduce the operating temperature and power consumption but also enhance the sensor's functionality by providing multivariable response signals, paving the way for intelligent gas detection. Based on the high selectivity to H2 and CO, transforming the transient sensing curves into two-dimensional images via Gramian Angular Field (GAF) model and subsequent modeling using a convolutional neural network (CNN) algorithm, we have realized efficient and intelligent recognition of H2 and CO. This work provides insight for the development of low-power, high-performance MEMS gas sensors and further intelligence of individual MEMS sensors.

Recent Advances, Challenges, and Future Perspectives of ZnO Nanostructure Materials Towards Energy Applications

In this approach, zinc oxide (ZnO) is a multipurpose substance with remarkable characteristics such as high sensitivity, a large specific area, non-toxicity, excellent compatibility, and a high isoelectric point, which make it attractive for discussion with some limitations. It is the most favorable possible option for the collection of nanostructures in terms of structure and their characteristics. The development of numerous ZnO nanostructure-based electrochemical sensors and biosensors used in health diagnosis, pharmaceutical evaluation, food hygiene, and contamination of the environment monitoring is described, as well as the production of ZnO nanostructures. Nanostructured ZnO has good chemical and temperature durability as an n-type semiconducting material, making it useful in a wide range of uses, from luminous materials to supercapacitors, batteries, solar cells, photocatalysis, biosensors, medicinal devices, and more. When compared to the bulk materials, the nanosized materials have both a higher rate of disintegration and a higher solubility. Furthermore, ZnO nanoparticles are regarded as top contenders for electrochemical sensors due to their strong electrochemical behaviors and electron transmission characteristics. The impact of many factors, including selectivity, sensitivity, detection limit, strength, and structures, arrangements, and their respective functioning processes, has been investigated. This study concentrated a substantial amount of its attention on the recent advancements that have been made in ZnO-based nanoparticles, composites, and modified materials for use in the application areas of energy storage and conversion devices as well as biological applications. Supercapacitors, Li-ion batteries, dye-sensitized solar cells, photocatalysis, biosensors, medicinal, and biological systems have been studied. ZnO-based materials are constantly analyzed for their advantages in energy and life science applications.

First-Order Individual Gas Sensors as Next Generation Reliable Analytical Instruments

It is conventionally expected that the performance of existing gas sensors may degrade in the field compared to laboratory conditions because (i) a sensor may lose its accuracy in the presence of chemical interferences and (ii) variations of ambient conditions over time may induce sensor-response fluctuations (i.e., drift). Breaking this status quo in poor sensor performance requires understanding the origins of design principles of existing sensors and bringing new principles to sensor designs. Existing gas sensors are single-output (e.g., resistance, electrical current, light intensity, etc.) sensors, also known as zero-order sensors (Karl Booksh and Bruce R. Kowalski, Analytical Chemistry, DOI: 10.1021/ac00087a718). Any zero-order sensor is undesirably affected by variable chemical background and sensor drift that cannot be distinguished from the response to an analyte. To address these limitations, we are developing multivariable gas sensors with independent responses, which are first-order analytical instruments. Here, we demonstrate self-correction against drift in two types of first-order gas sensors that operate in different portions of the electromagnetic spectrum. Our radiofrequency sensors utilize dielectric excitation of semiconducting metal oxide materials on the shoulder of their dielectric relaxation peak and achieve self-correction of the baseline drift by operation at several frequencies. Our photonic sensors utilize nanostructured sensing materials inspired by Morpho butterflies and achieve self-correction of the baseline drift by operation at several wavelengths. These principles of self-correction for drift effects in first-order sensors open opportunities for diverse emerging monitoring applications that cannot afford frequent periodic maintenance that is typical of traditional analytical instruments.

Molecularly Imprinted Polymer-Based Luminescent Chemosensors

Molecularly imprinted polymer (MIP)-based luminescent chemosensors combine the advantages of the highly specific molecular recognition of the imprinting sites and the high sensitivity with the luminescence detection. These advantages have drawn great attention during the past two decades. Luminescent molecularly imprinted polymers (luminescent MIPs) towards different targeted analytes are constructed with different strategies, such as the incorporation of luminescent functional monomers, physical entrapment, covalent attachment of luminescent signaling elements on the MIPs, and surface-imprinting polymerization on the luminescent nanomaterials. In this review, we will discuss the design strategies and sensing approaches of luminescent MIP-based chemosensors, as well as their selected applications in biosensing, bioimaging, food safety, and clinical diagnosis. The limitations and prospects for the future development of MIP-based luminescent chemosensors will also be discussed.

Soluble porous organic cages as homogenizers and electron-acceptors for homogenization of heterogeneous alloy nanoparticle catalysts with enhanced catalytic activity

The creation of ultrafine alloy nanoparticles (<5 nm) that can maintain surface activity and avoid aggregation for heterogeneous catalysis has received much attention and is extremely challenging. Here, ultrafine PtRh alloy nanoparticles imprisoned by the cavities of reduced chiral covalent imine cage (PtRh@RCC3) are prepared successfully by an organic molecular cage (OMC) confinement strategy, while the soluble RCC3 can act as a homogenizer to homogenize the heterogeneous PtRh alloy in solution. Moreover, the X-ray absorption near-edge structure (XANES) results show that the RCC3 can act as an electron-acceptor to withdraw electrons from Pt, leading to the formation of higher valence Pt atoms, which is beneficial to improving the catalytic activity for the reduction of 4-nitrophenol. Attributed to the synergistic effect of Pt/Rh atoms and the unique function of the RCC3, the reaction rate constants of Pt₁Rh₁₆@RCC3 are 49.6, 8.2, and 5.5 times than those of the Pt₁Rh₁₆ bulk, Pt@RCC3 and Rh@RCC3, respectively. This work provides a feasible strategy to homogenize heterogeneous alloy nanoparticle catalysts in solution, showing huge potential for advanced catalytic application.

Multilevel Self-Assembly of Block Copolymers and Polymer Colloids for a Transparent and Sensitive Gas Sensor Platform

The recent emerging significance of the Internet of Things (IoT) demands sensor devices to be integrated with many different functional structures and devices while conserving their original functionalities. To this end, optical transparency and mechanical flexibility of sensor devices are critical requirements for optimal integration as well as high sensitivity. In this work, a transparent, flexible, and sensitive gas sensor building platform is introduced by using multilevel self-assembly of block copolymers (BCPs) and polystyrene (PS) colloids. For the demonstration of an H₂ gas sensor, a hierarchically porous Pd metal mesh structure is obtained by overlaying the two different patterned template structures with synergistic, distinctive characteristic length scales. The hierarchical Pd mesh shows not only high transparency over 90% but also superior sensing performance in terms of response and recovery time owing to enhanced Pd-to-hydride ratio and short H₂ diffusion lengths from the enlarged active surface areas. The hierarchical morphology also endows high mechanical flexibility while securing reliable sensing performance even under severe mechanical deformation cycles. Our scalable self-assembly based multiscale nanopatterning offers an intriguing generalized platform for many different multifunctional devices requiring hidden in situ monitoring of environmental signals.

Machine Learning-Based Rapid Detection of Volatile Organic Compounds in a Graphene Electronic Nose

Rapid detection of volatile organic compounds (VOCs) is growing in importance in many sectors. Noninvasive medical diagnoses may be based upon particular combinations of VOCs in human breath; detecting VOCs emitted from environmental hazards such as fungal growth could prevent illness; and waste could be reduced through monitoring of gases produced during food storage. Electronic noses have been applied to such problems, however, a common limitation is in improving selectivity. Graphene is an adaptable material that can be functionalized with many chemical receptors. Here, we use this versatility to demonstrate selective and rapid detection of multiple VOCs at varying concentrations with graphene-based variable capacitor (varactor) arrays. Each array contains 108 sensors functionalized with 36 chemical receptors for cross-selectivity. Multiplexer data acquisition from 108 sensors is accomplished in tens of seconds. While this rapid measurement reduces the signal magnitude, classification using supervised machine learning (Bootstrap Aggregated Random Forest) shows excellent results of 98% accuracy between 5 analytes (ethanol, hexanal, methyl ethyl ketone, toluene, and octane) at 4 concentrations each. With the addition of 1-octene, an analyte highly similar in structure to octane, an accuracy of 89% is achieved. These results demonstrate the important role of the choice of analysis method, particularly in the presence of noisy data. This is an important step toward fully utilizing graphene-based sensor arrays for rapid gas sensing applications from environmental monitoring to disease detection in human breath.

Reversible assembly of nanoparticles: theory, strategies and computational simulations

The significant advances in synthesis and functionalization have enabled the preparation of high-quality nanoparticles that have found a plethora of successful applications. The unique physicochemical properties of nanoparticles can be manipulated through the control of size, shape, composition, and surface chemistry, but their technological application possibilities can be further expanded by exploiting the properties that emerge from their assembly. The ability to control the assembly of nanoparticles not only is required for many real technological applications, but allows the combination of the intrinsic properties of nanoparticles and opens the way to the exploitation of their complex interplay, giving access to collective properties. Significant advances and knowledge gained over the past few decades on nanoparticle assembly have made it possible to implement a growing number of strategies for reversible assembly of nanoparticles. In addition to being of interest for basic studies, such advances further broaden the range of applications and the possibility of developing innovative devices using nanoparticles. This review focuses on the reversible assembly of nanoparticles and includes the theoretical aspects related to the concept of reversibility, an up-to-date assessment of the experimental approaches applied to this field and the advanced computational schemes that offer key insights into the assembly mechanisms. We aim to provide readers with a comprehensive guide to address the challenges in assembling reversible nanoparticles and promote their applications.

Design of an Artificial Opal/Photonic Crystal Interface for Alcohol Intoxication Assessment: Capillary Condensation in Pores and Photonic Materials Work Together

Alcohol intoxication has a dangerous effect on human health and is often associated with a risk of catastrophic injuries and alcohol-related crimes. A demand to address this problem adheres to the design of new sensor systems for the real-time monitoring of exhaled breath. We introduce a new sensor system based on a porous hydrophilic layer of submicron silica particles (SiO₂ SMPs) placed on a one-dimensional photonic crystal made of Ta₂O₅/SiO₂ dielectric layers whose operation relies on detecting changes in the position of surface wave resonance during capillary condensation in pores. To make the active layer of SiO₂ SMPs, we examine the influence of electrostatic interactions of media, particles, and the surface of the crystal influenced by buoyancy, gravity force, and Stokes drag force in the frame of the dip-coating preparation method. We evaluate the sensing performance toward biomarkers such as acetone, ammonia, ethanol, and isopropanol and test sensor system capabilities for alcohol intoxication assessment. We have found this sensor to respond to all tested analytes in a broad range of concentrations. By processing the sensor signals by principal component analysis, we selectively determined the analytes. We demonstrated the excellent performance of our device for alcohol intoxication assessment in real-time.

Metal Nanocluster-Metal Organic Framework-Polymer Hybrid Nanomaterials for Improved Hydrogen Detection

The development of hydrogen sensors is of paramount importance for timely leak detection and remains a crucial unmet need. Palladium-based materials, well known as hydrogen sensors, still suffer from poisoning and deactivation. Here, a hybrid hydrogen sensor consisting of a Pd nanocluster (NC) film, a metal-organic framework (MOF), and a polymer, are proposed. The polymer coating, as a protection layer, endows the sensor with excellent H₂ selectivity and CO-poisoning resistance. The MOF serves as an interface layer between the Pd NC film and the polymer layer, which alters the nature of the interaction with hydrogen and leads to significant sensing performance improvements, owing to the interfacial electronic coupling between Pd NCs and the MOF. The strategy overcomes the shortcomings of retarded response speed and degraded sensitivity induced by the polymer coating of a Pd NC film-polymer hybrid system. This is the first exhibition of a hydrogen-sensing enhancement mechanism achieved by engineering the electronic coupling between Pd and a MOF. The work establishes a deep understanding of the hydrogen-sensing enhancement mechanism at the nanoscale and provides a feasible strategy to engineer next-generation gas-sensing nanodevices with superior sensing figures of merit via hybrid material systems.

State of the Art of Chemosensors in a Biomedical Context

Healthcare is undergoing large transformations, and it is imperative to leverage new technologies to support the advent of personalized medicine and disease prevention. It is now well accepted that the levels of certain biological molecules found in blood and other bodily fluids, as well as in exhaled breath, are an indication of the onset of many human diseases and reflect the health status of the person. Blood, urine, sweat, or saliva biomarkers can therefore serve in early diagnosis of diseases such as cancer, but also in monitoring disease progression, detecting metabolic disfunctions, and predicting response to a given therapy. For most point-of-care sensors, the requirement that patients themselves can use and apply them is crucial not only regarding the diagnostic part, but also at the sample collection level. This has stimulated the development of such diagnostic approaches for the non-invasive analysis of disease-relevant analytes. Considering these timely efforts, this review article focuses on novel, sensitive, and selective sensing systems for the detection of different endogenous target biomarkers in bodily fluids as well as in exhaled breath, which are associated with human diseases.

Identification of Chemical Vapor Mixture Assisted by Artificially Extended Database for Environmental Monitoring

A fully integrated sensor array assisted by pattern recognition algorithm has been a primary candidate for the assessment of complex vapor mixtures based on their chemical fingerprints. Diverse prototypes of electronic nose systems consisting of a multisensory device and a post processing engine have been developed. However, their precision and validity in recognizing chemical vapors are often limited by the collected database and applied classifiers. Here, we present a novel way of preparing the database and distinguishing chemical vapor mixtures with small data acquisition for chemical vapors and their mixtures of interest. The database for individual vapor analytes is expanded and the one for their mixtures is prepared in the first-order approximation. Recognition of individual target vapors of NO₂, HCHO, and NH₃ and their mixtures was evaluated by applying the support vector machine (SVM) classifier in different conditions of temperature and humidity. The suggested method demonstrated the recognition accuracy of 95.24%. The suggested method can pave a way to analyze gas mixtures in a variety of industrial and safety applications.

Revealing the Phase Segregation and Evolution Dynamics in Binary Nanoalloys via Electron Beam-Assisted Ultrafast Heating and Cooling

Gas-phase synthesized binary nanoparticles (NPs) possess ultraclean surfaces, which benefit versatile uses in sensors and catalysts. However, precise control of their configuration and properties is still a big challenge because the growth mechanism and phase evolution dynamics in these NPs are very hard to unveil. Here, we report a strategy to investigate the phase evolution dynamics in binary NPs by using e-beam assisted ultrafast local heating and cooling inside a transmission electron microscope. With this strategy, the phase segregation and corresponding shape evolution of PbBi NPs are in situ revealed. It is found that the as-prepared PbBi alloy NPs will transform into heterostructures under e-beam stimulated structural relaxation, leading to the formation of featured Janus configurations with faceted Bi polyhedron parts and intermetallic hemisphere parts. During phase segregation, Pb₁Bi₁ and Pb₇Bi₃ phases are captured and identified, and a model of phase and shape evolution of PbBi nanoalloys is developed and contrasted with that of their bulk counterparts. These findings benefit the understanding of the phase dynamics of binary NPs and can provide in-depth information for engineering their structures for practical applications.

Gold nanoparticles endowed with low-temperature colloidal stability by cyclic polyethylene glycol in ethanol

The colloidal stability of metal nanoparticles is tremendously dependent on the thermal behavior of polymer brushes. Neat polyethylene glycol (PEG) presents an unconventional upper critical solution temperature in ethanol, where phase segregation and crystallization coexist. This thermal behavior translated to a PEG brush has serious consequences on the colloidal stability in ethanol of gold nanoparticles (AuNPs) modified with PEG brushes upon cooling. We observed that AuNPs (13 nm diameter) stabilized with conventional linear PEG brushes (M_n = 6 and 11 kg mol^-1) in ethanol suffer from reversible phase separation upon a temperature drop over the course of a few hours. However, the use of a polymer brush with cyclic topology as a stabilizer prevents sedimentation, ensuring the colloidal stability in ethanol at -25 °C for, at least, four months. We postulate that temperature-driven collapse of chain brushes promotes the interpenetration of linear chains, causing progressive AuNP sedimentation, a process that is unfavorable for cyclic polymer brushes whose topology prevents chain interpenetration. This study reinforces the notion about the importance of polymer topology on the colloidal stability of AuNPs.

Lattice expansion and oxygen vacancy of α-Fe2O3 during gas sensing

Identifying the nature of gas-sensing material under the real-time operating condition is very critical for the research and development of gas sensors. In this work, we implement in situ Raman and XRD to investigate the gas-sensing nature of α-Fe₂O₃ sensing material, which derived from Fe-based metal-organic gel (MOG). The active mode of α-Fe₂O₃ as gas-sensing material originate from the thermally induced lattice expansion and the changes of surface oxygen vacancy of α-Fe₂O₃ could be reflected from the further monitored Raman scattering signals during acetone gas sensing. Meanwhile, the prepared α-Fe₂O₃ gas sensor exhibits excellent gas-sensing performance with high response value (R_a/R_g = 27), rapid response/recovery time (1 s/80 s) for 100 ppm acetone gas, and broad response range (5 - 900 ppm) at 183 °C. Strategies described herein could provide a promising approach to obtain gas-sensing materials with excellent performance and unveil the gas-sensing nature for other metal-oxide-based chemiresistors.

MOF-Mediated Synthesis of Supported Fe-Doped Pd Nanoparticles under Mild Conditions for Magnetically Recoverable Catalysis*

Metal-organic framework (MOF)-driven synthesis is considered as a promising alternative for the development of new catalytic materials with well-designed active sites. This synthetic approach is used here to gradually transform a new bimetallic MOF, with Pd and Fe as the metal components, by the in situ generation of aniline under mild conditions. This methodology results in a compositionally homogeneous nanocomposite formed by Fe-doped Pd nanoparticles that, in turn, are supported on iron oxide-doped carbon. The nanocomposite has been fully characterized by several techniques such as IR and Raman spectroscopy, TEM, XPS, and XAS. The performance of this nanocomposite as an heterogeneous catalyst for hydrogenation of nitroarenes and nitrobenzene coupling with benzaldehyde has been evaluated, proving it to be an efficient and reusable catalyst.

Bio-inspired gas sensing: boosting performance with sensor optimization guided by "machine learning"

The performance of existing gas sensors often degrades in field conditions because of the loss of measurement accuracy in the presence of interferences. Thus, new sensing approaches are required with improved sensor selectivity. We are developing a new generation of gas sensors, known as multivariable sensors, that have several independent responses for multi-gas detection with a single sensor. In this study, we analyze the capabilities of natural and fabricated photonic three-dimensional (3-D) nanostructures as sensors for the detection of different gaseous species, such as vapors and non-condensable gases. We employed bare Morpho butterfly wing scales to control their gas selectivity with different illumination angles. Next, we chemically functionalized Morpho butterfly wing scales with a fluorinated silane to boost the response of these nanostructures to the vapors of interest and to suppress the response to ambient humidity. Further, we followed our previously developed design rules for sensing nanostructures and fabricated bioinspired inorganic 3-D nanostructures to achieve functionality beyond natural Morpho scales. These fabricated nanostructures have embedded catalytically active gold nanoparticles to operate at high temperatures of ≈300 °C for the detection of gases for solid oxide fuel cell (SOFC) applications. Our performance advances in the detection of multiple gaseous species with specific nanostructure designs were achieved by coupling the spectral responses of these nanostructures with machine learning (a.k.a. multivariate analysis, chemometrics) tools. Our newly acquired knowledge from studies of these natural and fabricated inorganic nanostructures coupled with machine learning data analytics allowed us to advance our design rules for sensing nanostructures toward the required gas selectivity for numerous gas monitoring scenarios at room and high temperatures for industrial, environmental, and other applications.

"Optical tentacle" of suspended polymer micro-rings on a multicore fiber facet for vapor sensing

We designed a new type of gas sensor, an optical tentacle, made of highly integrated polymer micro-ring resonators in three-dimensional space on the tiny end-facet of a multicore optical fiber. Two pairs of three polymer micro-ring resonators were hung symmetrically on both sides of three suspended micro-waveguides as the sensing units. The micro-waveguides interlace to form a three-layer nested configuration, which makes the multicore optical fiber a "tentacle" for vapors of volatile organic compounds. Both experiments and theoretical simulation confirmed that the symmetrical coupling of multiple pairs of rings with the micro-waveguide had better resonance than the single ring setup. This is because the symmetrical light modes in the waveguides couple with the rings separately. All the optical micro-components were fabricated by the two-photon lithography technology on the end facet of multicore optical fiber. The optical tentacle shows good sensitivity and reversibility. This approach can also be adopted for sensor array design on a chip. Furthermore, optical sensors that can sense vapors with multiple constituents may be achieved in the future by adding selective sensitive materials to or on the surface of the rings.

A Colorimetric Artificial Olfactory System for Airborne Improvised Explosive Identification

The detection of ultralow or nonvolatile target analytes remains a significant challenge for artificial olfactory systems even after decades of development, which severely limits their widespread application. To overcome this challenge, an artificial olfactory system based on a colorimetric hydrogel array is constructed for the first time as a universal representative. As an effective extension of conventional artificial olfactory systems that integrates the merits of its predecessors, the proposed system accurately mimics olfactory mucosa and specific odorant binding proteins using hydrogels endowed with specific colorimetric reagents for the detection of hypochlorite, chlorate, perchlorate, urea, and nitrate. Therefore, the proposed system is capable of detecting and discriminating between these five airborne improvised explosive microparticulates with a detection limit as low as 39.4 pg. Additionally, the system demonstrates good reusability over ten cycles, rapid response time of ≈0.2 s, and excellent discrimination properties, despite significant variation. This proof-of-concept study on colorimetric artificial olfactory systems yields a novel strategy for the direct and discriminative detection of nonvolatile airborne microparticulates.

3D nanoporous plasmonic chips for extremely sensitive NO2 detection

The detection of toxic gas molecules using the surface-enhanced Raman spectroscopy (SERS) technique is very challenging due to the low affinity of gas molecules. Here, we report extremely sensitive SERS-based NO₂ gas sensors based on 3D nanoporous Au nanostructures with a high affinity for NO₂ gas molecules and high density of hotspots.

Structure Evolution of Binary Ligands on Nanoparticles Triggered by Competition between Adsorption Reaction and Phase Separation

The ligand shell of a nanoparticle (NP) determines most of the interfacial properties through its composition and structure. Despite widespread study over the years, the factors impacting the ligand shell structures, especially the effects of ligand-adsorption kinetics in solution, are still not clear and even conflict with each other. We have developed an adsorption-migration reaction model to study the dynamic evolution processes of binary ligands on NP surfaces during adsorption reaction. Apparent dependence of the structure of ligand shells on ligand-adsorption and phase-separation rates has been found, which induces the formation of different shell patterns, including Janus, patchy, stripe, and island patterns. The formation process of these patterns accords with different reaction kinetic pathways, depending on the nature of ligands. Further screening the role of the NPs' curvature reveals that it can indirectly influence the ligand-adsorption and phase-separation kinetics. As the NPs' curvature increases, an accelerated ligand-adsorption and phase-separation process on NPs will happen, resulting in the preferential formation of more ordered Janus or stripe patterns. These results suggest that controlling the reaction kinetics is key to effectively regulating the composition and morphology of binary ligands on NPs. They also provide principles for guiding the experimental studies to fabricate novel NPs with a functional surface for use in broad nanoscience fields.

Cyclodextrin polymer networks decorated with subnanometer metal nanoparticles for high-performance low-temperature catalysis

The synthesis of support materials with suitable coordination sites and confined structures for the controlled growth of ultrasmall metal nanoparticles is of great importance in heterogeneous catalysis. Here, by rational design of a cross-linked β-cyclodextrin polymer network (CPN), various metal nanoparticles (palladium, silver, platinum, gold, and rhodium) of subnanometer size (<1 nm) and narrow size distribution are formed via a mild and facile procedure. The presence of the metal coordination sites and the network structure are key to the successful synthesis and stabilization of the ultrasmall metal nanoparticles. The as-prepared CPN, loaded with palladium nanoparticles, is used as a heterogeneous catalyst and shows outstanding catalytic performance in the hydrogenation of nitro compounds and Suzuki-Miyaura coupling reaction under mild conditions. The CPN support works synergistically with the metal nanoparticles, achieving high catalytic activity and selectivity. In addition, the catalytic activity of the formed catalyst is controllable.

Charged Metal Nanoparticles for Chemoelectronic Circuits

Although metal nanoparticles (NPs) stabilized with self-assembled monolayers (SAMs) of various organic ligands have proven useful in applications ranging from chemical sensing, to bionanotechnology, to plasmonics and energy conversion, they have not been widely considered as suitable building blocks of electronic circuitry, largely because metals screen electric fields and prevent electrically tunable conductivity. However, when metal nanoparticles a few nanometers in size are stabilized by charged ligands and placed under bias, the counterions surrounding the NPs can redistribute and establish local electric fields that feed back into the electronic currents passing through the nanoparticles' metallic cores. Herein, the manner in which the interplay between counterion gradients and electron flows can be controlled by using different types of SAMs is discussed. This can give rise to a new class of nanoparticle-based "chemoelectronic" logic circuits capable of sensing, processing, and ultimately reporting various chemical signals.

Classification of Tea Aromas Using Multi-Nanoparticle Based Chemiresistor Arrays

Nanoparticle based chemical sensor arrays with four types of organo-functionalized gold nanoparticles (AuNPs) were introduced to classify 35 different teas, including black teas, green teas, and herbal teas. Integrated sensor arrays were made using microfabrication methods including photolithography and lift-off processing. Different types of nanoparticle solutions were drop-cast on separate active regions of each sensor chip. Sensor responses, expressed as the ratio of resistance change to baseline resistance (ΔR/R₀), were used as input data to discriminate different aromas by statistical analysis using multivariate techniques and machine learning algorithms. With five-fold cross validation, linear discriminant analysis (LDA) gave 99% accuracy for classification of all 35 teas, and 98% and 100% accuracy for separate datasets of herbal teas, and black and green teas, respectively. We find that classification accuracy improves significantly by using multiple types of nanoparticles compared to single type nanoparticle arrays. The results suggest a promising approach to monitor the freshness and quality of tea products.

Superwettable colloidal crystal micropatterns on butterfly wing surface for ultrasensitive detection

HYPOTHESIS

Ultrasensitive detections with enrichment approaches based on hydrophilic-hydrophobic patterns have attracted increasing attention in the early diagnosis and treatment of diseases. However, most of these techniques involve complicated micro-fabrications and chemical modifications to achieve their specific pattern substrate wettability. Hence, the development of a simple and effective approach for the construction of new surface wettability techniques for ultrasensitive detection is with great expectations.

EXPERIMENTS

We present a simple approach to fabricate the superwettable colloidal crystal (CC) micropatterns on superhydrophobic Morpho butterfly wing surface for the ultrasensitive detection. The superwettable CC micropatterns were easily obtained by infiltrating and self-assembling monodispersed silica colloidal nanoparticles on the plasma treated butterfly wing patterns. The analytes could be enriched onto the hydrophilic CC area due to the wettability difference between the hydrophilic CC area and the superhydrophobic substrate.

FINDINGS

It was demonstrated that the detection limit of thrombin was down to 1.8 × 10^-13 mol L^-1 based on the fluorophore-labeled aptamer. Moreover, with two-dimensional position codes of these CC micropatterns for different probes, the multiplex detection capability was also demonstrated with great accuracy. As the elimination of complex instruments and chemical modifications, this proposed platform offers a simple strategy for ultrasensitive multiplex detection in practical applications.

Cardiomyocytes-Actuated Morpho Butterfly Wings

Morpho butterflies are famous for their wings' brilliant structural colors arising from periodic nanostructures, which show great potential value for fundamental research and practical applications. Here, a novel cellular mechanical visualizable biosensor formed by assembling engineered cardiac tissues on the Morpho butterfly wings is presented. The assembled cardiomyocytes benefit from the periodic parallel nanoridges of the wings and can recover their autonomic beating ability with guided cellular orientation and good contraction performance. As the beating processes are accompanied by the cardiomyocytes' elongation and contraction, the elastic butterfly wing substrate undergoes the same cycle of deformations, which causes corresponding synchronous shifts in their structural colors and photonic bandgaps for self-reporting of the cell mechanics. It is demonstrated that this self-reporting performance can be further improved by adding oriented carbon nanotubes in the nanoridges of the wings for the culture. In addition, taking advantage of the similar size of the cardiomyocyte and a single Morpho wing scale, the investigation of single-cell-level mechanics can be realized by detecting the optical performance of a single scale. These remarkable properties make these butterfly wings ideal platforms for biomedical research.

Ligand-Capped Ultrapure Metal Nanoparticle Sensors for the Detection of Cutaneous Leishmaniasis Disease in Exhaled Breath

Human cutaneous leishmaniasis, although designated as one of the most neglected tropical diseases, remains underestimated due to its misdiagnosis. The diagnosis is mainly based on the microscopic detection of amastigote forms, isolation of the parasite, or the detection of Leishmania DNA, in addition to its differential clinical characterization; these tools are not always available in routine daily practice, and they are expensive and time-consuming. Here, we present a simple-to-use, noninvasive approach for human cutaneous leishmaniasis diagnosis, which is based on the analysis of volatile organic compounds in exhaled breath with an array of specifically designed chemical gas sensors. The study was realized on a group of n = 28 volunteers diagnosed with human cutaneous leishmaniasis and a group of n = 32 healthy controls, recruited in various sites from Tunisia, an endemic country of the disease. The classification success rate of human cutaneous leishmaniasis patients achieved by our sensors test was 98.2% accuracy, 96.4% sensitivity, and 100% specificity. Remarkably, one of the sensors, based on CuNPs functionalized with 2-mercaptobenzoxazole, yielded 100% accuracy, 100% sensitivity, and 100% specificity for human cutaneous leishmaniasis discrimination. While AuNPs have been the most extensively used in metal nanoparticle-ligand sensing films for breath sensing, our results demonstrate that chemical sensors based on ligand-capped CuNPs also hold great potential for breath volatile organic compounds detection. Additionally, the chemical analysis of the breath samples with gas chromatography coupled to mass spectrometry identified nine putative breath biomarkers for human cutaneous leishmaniasis.

Tracking Molecular Diffusion in One-Dimensional Photonic Crystals

The intuitive use, inexpensive fabrication, and easy readout of colorimetric sensors, including photonic crystal architectures and Fabry-Pérot interference sensors, have made these devices a successful commercial case, and yet, understanding how the diffusion of analytes occurs throughout the structure is a key ingredient for designing the response of these platforms on demand. Herein, the diffusion of amines in a periodic multilayer system composed of two-dimensional nanosheets and dielectric nanoparticles is tracked by a combination of spectroscopic measurements and theoretical modelling. It is demonstrated that diffusion is controlled by the molecular size, with larger molecules showing larger layer swelling and slower diffusion times, which translates into important sensor characteristics such as signal change and saturation time. Since the approach visualizes the analyte impregnation process in a time- and spatially resolved fashion, it directly relates the macroscopic color readout into microscopic processes occurring at the molecular level, thus opening the door to rational sensor design.

Synergy between nanomaterials and volatile organic compounds for non-invasive medical evaluation

This article is an overview of the present and ongoing developments in the field of nanomaterial-based sensors for enabling fast, relatively inexpensive and minimally (or non-) invasive diagnostics of health conditions with follow-up by detecting volatile organic compounds (VOCs) excreted from one or combination of human body fluids and tissues (e.g., blood, urine, breath, skin). Part of the review provides a didactic examination of the concepts and approaches related to emerging sensing materials and transduction techniques linked with the VOC-based non-invasive medical evaluations. We also present and discuss diverse characteristics of these innovative sensors, such as their mode of operation, sensitivity, selectivity and response time, as well as the major approaches proposed for enhancing their ability as hybrid sensors to afford multidimensional sensing and information-based sensing. The other parts of the review give an updated compilation of the past and currently available VOC-based sensors for disease diagnostics. This compilation summarizes all VOCs identified in relation to sickness and sampling origin that links these data with advanced nanomaterial-based sensing technologies. Both strength and pitfalls are discussed and criticized, particularly from the perspective of the information and communication era. Further ideas regarding improvement of sensors, sensor arrays, sensing devices and the proposed workflow are also included.

Cataluminescence sensing of carbon disulfide based on CeO2 hierarchical hollow microspheres

Material morphology-dependent cataluminescence (CTL) sensing characteristic and application are presented in this work. Hierarchical hollow microspheres CeO₂ were synthesized via the hydrothermal reaction of glucose and N, N-dimethyl-formamide (Glu-DMF). SEM, XRD, TEM, HRTEM and BET were used to characterize the prepared CeO₂ materials. Compared with CeO₂ cubics (CeO₂ Cubs), CeO₂ hierarchical hollow microspheres (CeO₂ HMs) show an enhanced CTL response to carbon disulfide. The response and recovery times of CeO₂ HMs-based CTL sensor towards carbon disulfide are about 8 s and 20 s, respectively. CeO₂ HMs exhibits a linear CTL response to carbon disulfide in the concentration range of 0.50~10 μg•mL^-1 with an excellent sensitivity and selectivity. These results suggest that CeO₂ HMs will be a highly promising CTL sensing material for the detection and monitoring carbon disulfide. Graphical abstract CeO₂ hierarchical hollow microspheres (CeO₂ HMs) were synthesized via the hydrothermal reaction of glucose and N, N-dimethyl-formamide (Glu-DMF). Meanwhile, the prepared CeO₂ HMs shows commendable CTL response towards carbon disulfide. Due to the excellent analytical performance of designed CeO₂ HMs-based sensor for carbon disulfide, it has potential application value in various locations.

A novel flurophore-cyano-carboxylic-Ag microhybrid: Enhanced two photon absorption for two-photon photothermal therapy of HeLa cancer cells by targeting mitochondria

In this study, a novel two-photon photothermal therapy (TP-PTT) agent based on an organic-metal microhybrid with surface Plasmon resonance (SPR) enhanced two-photon absorption (TPA) characteristic was designed and synthesized using a fluorescent cyano-carboxylic derivative 2-cyano-3-(9-ethyl-9H-carbazol-3-yl) -acrylic acid (abbreviated as CECZA) and silver nanoparticles through self-assembly process induced by the interfacial coordination interactions between the O/N atom of CECZA and Ag⁺ion at the surface of Ag nanoparticles. The coordination interactions caused electron transfer from the Ag nanoparticles to CECZA molecules at the excited state, resulting in a decreased fluorescence quantum yield. The interfacial coordination interactions also enhanced the nonlinear optical properties, including 13 times increase in the TPA cross-section (δ). The decreased fluorescence quantum yield and increased two photon absorption caused by the SPR effect led excellent two-photon photothermal conversion, which was beneficial for the TP-PTT effect on HeLa cancer cells.

Exploration of photothermal sensors based on photothermally responsive materials: a brief review

Photothermal sensors have emerged as a new type of sensor platform in recent decades and this brief review has summarized different types of photothermally responsive materials and their applications in various fields.