Sensor Performance Research Articles

CO2 Interaction Mechanism of SnO2-Based Sensors with Respect to the Pt Interdigital Electrodes Gap

The tuning sensitivity towards CO2 detection under in-field-like conditions was investigated using SnO2-sensitive material deposited onto Al2O3 substrates provided with platinum electrodes with interdigital gaps of 100 µm and 30 µm. X-ray diffraction, low-magnification and high-resolution transmission electron microscopy, and electrical and contact potential difference investigations were employed to understand the sensing mechanism involved in CO2 detection. The morpho-structural analysis revealed that the SnO2 nanoparticles exhibit well-defined facets along the (110) and (101) crystallographic planes. Complex phenomenological investigations showed that moisture significantly affects the gas sensing performance. The experimental results corroborated the literature evidence, highlighting the importance of Pt within the interdigital electrodes subsequently reflected in the increase in the CO2 sensing performance with the decrease in the interdigital gap. The catalytic efficiency is explained by the distribution of platinum at the gas-Pt-SnO2 three-phase boundary, which is critical for enhancing the sensor performance.

Performance of a MEMS Flow Sensor With High Temperature

Performance of a MEMS Flow Sensor With High Temperature

Design Rules for 3D Printing‐Assisted Pressure Sensor Manufacturing: Achieving Broad Pressure Range Linearity

AbstractRecent advancements in 3D printing technology have expanded its application to manufacturing pressure sensors. By harnessing the cost‐effectiveness, streamlined processes, and design flexibility of 3D printing, sensor fabrication can be customized to meet specific performance needs. Thus far, 3D printing in pressure sensor development has been primarily limited to creating molds for transferring patterns onto flexible substrates, restricting both material selection and sensor performance. To fully unlock the potential of 3D printing in advanced pressure sensor fabrication, it is crucial to establish effective design rules focused on enhancing the figure of merit performance. This study introduces a universal design strategy aimed at maintaining high sensitivity across a wide pressure range—a challenging feat, as sensitivity significantly decreases at higher pressures. Our approach centers on engineering the deformability of 3D‐printed structures, achieving a linear increase in contact area between sensor patterns and electrodes without reaching saturation. Sensors designed with high elongation and low stiffness exhibit consistent sensitivity of 162.5 kPa⁻¹ across a broad pressure range (0.05–300 kPa). Mechanistic investigations through finite element analysis confirm that engineered deformability is key to achieving this enhanced linear response, offering robust sensing capabilities for demanding applications such as deep‐sea and space exploration.

Construction of Mo-Doped CuO Incorporated on Carbon Black Modified Disposable Sensor for the Voltammetric Detection of Metol in Environmental Samples

Abstract In this study, a molybdenum-doped copper oxide (Mo–CuO) composite was synthesized via a hydrothermal method and combined with carbon black (CB) to form Mo–CuO@CB. This composite was used to modify a screen-printed carbon electrode (SPCE) for the detection of Metol (MT), an industrial pollutant harmful to both human health and the environment. Structural and surface characterization was performed using high-resolution transmission electron microscopy, field-effect scanning electron microscopy, energy-dispersive spectroscopy, Raman spectroscopy, X-rssy photoelectron spectroscopy, and X-ray diffraction. Electrochemical techniques, including differential pulse voltammetry and cyclic voltammetry, were used to assess the sensor's performance. The Mo–CuO@CB@SPCE sensor exhibited a low detection limit of 2.7 nM, and limit of quantification is 82 nM, a broad linear range (5.0 × 10−9 – 170 mol L-1), and high sensitivity (4.148 μA μM⁻¹ cm⁻²), benefiting from the catalytic activity of Mo–CuO and the large surface area of CB. With recovery rates ranging from 96 % to 100.6% in pond, river, and tap water, the sensor effectively detects MT in environmental samples, ensuring reliable monitoring of this persistent pollutant.

Zn-Modified TiO2 Thin-Films for Real-Time Formaldehyde Sensing at Room Temperature

Abstract This study explores the fabrication and application of zinc-modified titanium dioxide (Zn-TiO2) thin-films for real-time recognition of volatile organic compounds (VOCs), with a particular emphasis on formaldehyde (HCHO) sensing at room temperature. The Zn-TiO2 thin-films were produced using an economical spray-pyrolysis method. Structural, morphological, and optical characterizations confirmed the successful integration of zinc with varied Wt.% (0, 2, 4, and 6) into the TiO2 lattice. The real-time monitoring capabilities of the sensors were assessed against a range of VOCs, highlighting its specificity for formaldehyde detection amidst diverse environmental constituents. The fabricated thin film sensors with zinc dopant were optimized to enhance the sensor's performance. 4 Wt.% Zn-TiO2 demonstrated excellent sensitivity to formaldehyde vapor at ambient conditions, showcasing a rapid and selective response. The underlying sensing mechanism was explored, emphasizing the role of zinc doping in tailoring the material's surface properties and facilitating enhanced adsorption of formaldehyde molecules. The study underscores the potential of Zn-modified TiO2 thin films as a reliable and efficient platform for real-time VOC monitoring, with a specific focus on HCHO sensing at room-temperature. The sensor shows remarkable stability and repeatability, making it a promising candidate for continuous monitoring applications.

Acetone Gas Sensor with SnO2-Modified MoS2 Nanospheres

Abstract This study employed a hydrothermal method to prepare molybdenum disulfide (MoS2) and a two-step hydrothermal process to synthesize tin oxide (SnO2)-modified MoS2 nanostructured materials. The manufacturing process is simple and cost-effective, and the produced materials were analyzed using various techniques to confirm their high purity and crystallinity. The SnO2-modified MoS2 nanostructured materials were then utilized to fabricate acetone gas sensors. The high surface area of MoS2, coupled with the heterojunction interfaces formed by SnO2 modification, enhances the performance of the gas sensor. At 150°C, the sensor exhibits a remarkable response of 37.1% to 100 ppm acetone gas. The dynamic response, including response and recovery times, is also impressive. Gas sensors developed with this material can effectively detect acetone concentrations in various environmental conditions.

Deep Learning-Enabled De-Noising of Fiber Bragg Grating-Based Glucose Sensor: Improving Sensing Accuracy of Experimental Data

This paper outlines the successful utilization of deep learning (DL) techniques to elevate data quality for assessing Au-TFBG (tilted fiber Bragg grating) sensor performance. Our approach involves a well-structured DL-assisted framework integrating a hierarchical composite attention mechanism. In order to mitigate high variability in experimental data, we initially employ seasonal decomposition using moving averages (SDMA) statistical models to filter out redundant data points. Subsequently, sequential DL models extrapolate the normalized transmittance (Tn) vs. wavelength spectra, which showcases promising results through our SpecExLSTM model. Furthermore, we introduce the AttentiveSpecExLSTM model, integrating a composite attention mechanism to improve Tn sequence prediction accuracy. Evaluation metrics demonstrate its superior performance, including a root mean square error of 1.73 ± 0.05, a mean absolute error of 1.20 ± 0.04, and a symmetric mean absolute percentage error of 2.22 ± 0.05, among others. Additionally, our novel minima difference (Min. Dif.) metric achieves a value of 1.08 ± 0.46, quantifying wavelength for the global minima within the Tn sequence. The composite attention mechanism in the AttentiveSpecExLSTM adeptly captures both high-level and low-level dependencies, refining the model’s comprehension and guiding informed decisions. Hierarchical dot and additive attention within this model enable nuanced attention refinement across model layers; dot attention focuses on high-level dependencies, while additive attention fine-tunes its focus on low-level dependencies within the sequence. This innovative strategy enables accurate estimation of the spectral width (full-width half maxima) of the Tn curve, surpassing raw data’s capabilities. These findings significantly contribute to data quality enhancement and sensor performance analysis. Insights from this study hold promise for future sensor applications, enhancing sensitivity and accuracy by improving experimental data quality and sensor performance assessment.

Indium Oxide Thick-Films Decorated with PdO and PtO2 Particles for Oxygen Detection in Humid Environments

Abstract Thick film indium oxide chemiresistive sensors decorated with PdO and PtO2 particles were investigated for oxygen detection under humid conditions (tested ranging from 20%- 80% RH) across a temperature range of 50 °C to 400 °C. The PtO2-decorated In2O3 sensors demonstrated a significantly higher response to oxygen, showing a 500% increase at 200 °C compared to the PdO-decorated sensors (response values of 41 and 8, respectively). Tests in dry air were conducted to assess the effect of humidity on sensor performance, revealing a maximum response of 74 for PtO2-In2O3 at 400 °C, more than three times higher than the response of 22 for PdO-In2O3. Selectivity tests confirmed that the sensors responded more strongly to oxygen than to interfering gases. The integration of an active carbon cloth (ACC) filter effectively reduced interferences from isobutylene and ethylene, enhancing sensor’s selectivity. A comparison of both sensors demonstrated that PtO2-decorated In2O3 has greater potential as an alternative to existing Pb-based electrochemical oxygen sensors, particularly in humid environments.

Silk Flocked Flexible Sensor Capable of Wide-Range and Sensitive Pressure Perception.

In recent years, there have been advancements in high-performance soft sensors with simultaneous moderate sensitivity and wide linearity. However, it remains challenging to combine high-efficiency production and high performance for soft sensors. The skin and hair structure provide an elegantly simple sensing model, where hair acts as signal receptors and basal skin acts as signal processors. Herein, we used efficient electrostatic flocking and extrusion printing to engineer comb-shaped electrodes with conductive polydimethylsiloxane (PDMS) flocked with conductive silk fibers as a biomimetic flexible sensor. The mechanical and electrical properties of modified PDMS and silk fibers were characterized to optimize the functionalization process. The sensor unit exhibited a high linear range up to 2,000 kPa and a competitively good sensitivity of 0.0285 kPa-1 in the contact mode with conductive materials, as well as good resolution in the noncontact mode. Such sensors and a sensor array demonstrated potential applications for detecting pressure disturbances from acoustic activity and for human-robot interactions. We anticipate that the straightforward design and facile fabrication of soft sensors with vertical fibrous morphology for perception will open new avenues for the next generation of high-performance soft sensors integrated with artificial intelligence.

Organic‐Based Chemiresistive Sensors for Detection of Water‐Soluble Gases: Strategies and Roadmap for Enhancing Sensing Performance

AbstractTraditional organic‐based chemiresistive sensors have been a key area of research due to their portability, low power consumption, low cost, turnability, and possibility for miniaturization. However, their real‐world applications have remained restricted by their low selectivity, low sensitivity, and low stability under demanding conditions, such as extreme temperature, humidity, and pH. As such, this review aims to lay the foundation for enhancing the performance of these gas sensors via chemical and physical modifications. To this end, an insight into the building blocks of chemiresistive gas sensors and the attributes of the main four gases (ammonia, carbon dioxide, hydrogen sulfide, and nitrogen dioxide) under aqueous conditions are provided. Such features ultimately determine the enhancement strategy that is best suited to improve the chemiresistive gas sensors performance. Furthermore, this article provides an outlook into the current bottleneck in sensor development and its translation from lab to end‐consumer use. Overall, this review aims to serve as a roadmap for developing next‐generation, high performing chemiresistive gas sensors.

Exceptional Electrical Detection of Trace NO2 via Mixed Metal MOF-on-MOF Film-Based Sensors.

The tunability of metal-organic frameworks (MOFs) makes them exceptional materials for the development of highly selective, low-power sensors for toxic gas detection. Herein, we demonstrate enhanced detection of NO2 gas by a MOF-based electrical impedance sensor made using a unique mixed metal MOF-on-MOF synthesis. A combined experimental and computational study was performed using the exemplar NixMg1-x-MOF-74 to understand the fundamental structure-property relationships behind metal mixing and MOF film synthesis methods on sensor performance. Density functional theory results indicated that the presence of Ni in Mg-MOF-74 increased framework stability and increased the electron density of states at lower energies near the HOMO, as well as enhanced the NO2-Mg adsorption interaction. Impedance data of the NixMg1-x-MOF-74 films with larger Ni contents showed greater impedance change after exposure to 1 ppm of NO2 gas. Furthermore, when synthesized through either a drop-cast or direct solvothermal film growth approach, the monometallic Ni-based sensors had the best performance. However, the mixed metal NixMg1-x-MOF-74 sensors synthesized through a MOF-on-MOF approach resulted in the highest impedance change, outperforming all monometallic Ni-based sensors. In particular, the mixed metal Ni-on-Mg-MOF-74 film was the best-performing sensor with an impedance change of 309 upon trace NO2 exposure. Change in impedance response after NO2 exposure was improved by 52% compared to the best monometallic Ni-on-Ni-MOF-74 sensor. Structural analysis of the Ni-on-Mg film showed that the first Mg-MOF-74 layer acts as a structural template controlling the structural features of the final film after metal exchange with Ni. This led to improved film quality, evidenced by the greater crystallinity and larger MOF grain sizes, and resulted in enhanced sensor performance which was not achievable through other metal mixing methods. Altogether, this study identifies structure-property relationships and synthetic templating methods that inform MOF-based sensor design, allowing for improved detection of toxic compounds.

CRISPR-Cas14a and allosteric transcription factors empowered cell-free electrochemical biosensor for highly sensitive and stable detection of progesterone in multiple scenarios

In this study, a cell-free electrochemical assay based on allosteric transcription factors (aTFs) and CRISPR-Cas14a was developed for the detection of progesterone in trace samples. This electrochemical biosensor helps to overcome the drawbacks of the traditional fluorescence assay based on the CRISPR-Cas system and aTFs combined for non-nucleic acid targets that is poorly effective for the detection of colored samples. By comparing and optimizing the concentration and length of the probes in the straight chain and hairpin structure, the sensor performance was improved. In addition, different sgRNA from other studies was designed to overcome the effect of sequence folding in the space region on Cas14a activation. Based on these optimization results, we constructed an electrochemical sensor for progesterone quantification in the range of 66.7pM to 3.33 × 10−1μM. This method requires only 2 μL of sample and does not necessitate complex pretreatment steps, with detection completed within 1.5 h. The method has been successfully applied to food, environmental, and biological samples, with recovery rates between 82.65% and 109%. This suggests that CRISPR and allosteric transcription factor-powered electrochemical detection methods have significant potential for use in the field of small molecule detection under various scenarios.

Highly Elastic, Fatigue-Resistant, and Antifreezing MXene Functionalized Organohydrogels as Flexible Pressure Sensors for Human Motion Monitoring.

Conductive organohydrogels-based flexible pressure sensors have gained considerable attention in health monitoring, artificial skin, and human-computer interaction due to their excellent biocompatibility, wearability, and versatility. However, hydrogels' unsatisfactory mechanical and unstable electrical properties hinder their comprehensive application. Herein, an elastic, fatigue-resistant, and antifreezing poly(vinyl alcohol) (PVA)/lipoic acid (LA) organohydrogel with a double-network structure and reversible cross-linking interactions has been designed, and MXene as a conductive filler is functionalized into organohydrogel to further enhance the diverse sensing performance of flexible pressure sensors. The as-fabricated MXene-based PVA/LA organohydrogels (PLBM) exhibit stable fatigue resistance for over 450 cycles under 40% compressive strain, excellent elasticity, antifreezing properties (<-20 °C), and degradability. Furthermore, the pressure sensors based on the PLBM organohydrogels show a fast response time (62 ms), high sensitivity (S = 0.0402 kPa-1), and excellent stability (over 1000 cycles). The exceptional performance enables the sensors to monitor human movements, such as joint flexion and throat swallowing. Moreover, the sensors integrating with the one-dimensional convolutional neural networks and the long-short-term memory networks deep learning algorithms have been developed to recognize letters with a 93.75% accuracy, representing enormous potential in monitoring human motion and human-computer interaction.

Comprehensive and Robust Anti-Jamming Dual-Electrode Pair Sensor.

Capacitive flexible sensors often encounter instability caused by temperature fluctuations, electromagnetic interference, stray capacitance effects, and signal noise induced by ubiquitous vibrations. The challenge lies in achieving comprehensive anti-jamming abilities while preserving a simplistic structure and manufacturing process. To tackle this dilemma, a straightforward and effective design is utilized to achieve comprehensive and robust anti-jamming properties in capacitive sensors. Electrospinning thermoplastic polyurethane (TPU) fiber mats soak with ionic liquid (IL) to create a co-continuous structure (TPU@IL) with high ionic conductivity and dielectric constant, which acts as the sensing units. The sensing mechanism of the TPU@IL with multiple electrode pairs encapsulated by polyethylene terephthalate (PET) is systematically elucidated. The optimal dual-electrode pair design for capacitive and resistive sensors, which have different sensitivities to temperature and stress, simultaneous realizes temperature-stress dual-mode sensing. Remarkably, the sensitivity curve of the TPU@IL/PET capacitive sensor exhibits an intriguing rarely reported S-shape with an adjustable step stress point. No liquid leakage even during extensive stress-strain cycling (>4000 cycles). Despite a slight compromise in sensitivity and response time, the TPU@IL/PET sensor demonstrates exceptional electromechanical stability, reliability, and powerful anti-jamming abilities against various interferences. A simple yet innovative sensor design enhances the performance and applicability of capacitive sensors in challenging environments.

Molecularly imprinted electrochemical sensor based on APTES-functionalized indium tin oxide electrode for the determination of sulfadiazine.

Anelectrochemical sensor was developedfor the sensitive and selective detection of sulfadiazine(SDZ), based on a molecularly imprinted polymer (MIP) film formed on an indium tin oxide (ITO) electrode through a self-assembly process. The SDZ-imprinted ITO electrode (SDZ-MIP/APTES-ITO) was prepared through in situ polymerization using sulfadiazine, methacrylic acid (MAA), ethylene glycol dimethacrylate (EGDMA), and 2,2'-azobisisobutyronitrile (AIBN) as the template, functional monomer, cross-linker, and initiator respectively. Before polymerization, the ITO electrode was functionalized with 3-aminopropyltriethoxysilane (APTES) to promote covalent attachment of the polymer to the electrode. After polymerization, the template molecule SDZ was removed to create selective recognition sites, forming the molecularly imprinted polymer electrode (MIP/APTES-ITO), which facilitates sulfadiazine detection. The sensor's performance was evaluated using cyclic and differential pulse voltammetry, demonstrating a linear response in the sulfadiazine concentration range 0.1 to 300μM, with a detection limit of 0.11μM. The MIP-based sensor exhibited goodreproducibility, repeatability, selectivity, and stability in sulfadiazine detection. Its practical applicability was confirmed by the successful quantification of sulfadiazine in spiked milk samples.

2D heterostructures of graphene oxide and MoS2 to improve sensitivity performance of flexible pressure sensor with shell biological structure

Inspired by the multilayer structure of the shellfish, a novel two-dimensional (2D) composite structure consisting of graphene oxide, MoS2 and graphene oxide (G/M/G) was integrated to the flexible pressure sensing. The composite structure was prepared by the vacuum suction filtration in order to imitate the high toughness of shellfish. Based on the strategy of strain engineering, a comprehensive experimental bench capable of meeting different strain conditions was connected to a push–pull meter and an LCR digital bridge, and then a computer was used to record the changes in transducer capacitance when an external load was applied to the meter. Graphene oxide (GO) and G/M/G supercells were constructed, and density functional theory (DFT) simulations of the initial energy band structure under strain-free conditions were carried out to determine the relationship between the band gap, conductivity, capacitance, and sensitivity of the G/M/G structure based on band gap theory, and to easily understand the mechanism of the shellfish heterostructure leading to enhanced sensitivity of the sensors. Using two-point bending and axial tensile tests, a flexible pressure sensing device was designed to realize positive strains in the ranges of 0%–5 % and 5%–50 %. The results show that the sensitivity of both GO and G/M/G capacitive pressure sensors decreased with increased strain, and the sensitivity of the G/M/G sensors was significantly improved by 75%–102 % compared to the GO sensors at the same strain. The parallel-plate capacitor model and crack growth theory well explain the experimental results at small and large strains. Our results provide new concepts for the design of novel flexible sonar with high sensitivity and large strain operating range by a simple vacuum filtration method, which can be extended to the design of other high-performance 2D flexible electronic devices.

Innovative design and sensing performance of a novel large-strain sensor for prestressed FRP plates

Owing to the low measuring range of traditional fiber Bragg grating (FBG) sensors and the complexity, imprecision, and lack of practicality in existing large-strain sensors, this paper introduces a novel type of large-strain sensor based on pre-relaxation and continuous sensing technology. This design aims to realize the whole-process strain monitoring of prestressed fiber reinforced polymer (FRP) plates. Static tensile tests were conducted on 9 large-strain sensor specimens. The effects of pre-relaxation degree, section number, pre-tension time, and prestressing level were evaluated. The results reveal that, the pre-relaxed sensor, proved its efficacy in capturing the strain pertinent to the post-tensioned operational state of the FRP plate, and a sensing performance of up to 18,923 με can be facilitated and further extended by a pre-relaxation and continuous sensing technique. Moreover, it is recommended that during the fabrication of the large-strain sensor, two pre-tensions be applied, with a prestressing level between 115% and 125% of the pre-relaxation degree.

Metal–Organic Framework-Derived CeO2/Gold Nanospheres in a Highly Sensitive Electrochemical Sensor for Uric Acid Quantification in Milk

In this work, CeBTC (a cerium(III) 1,3,5-benzene-tricarboxylate), was used as a precursor for obtaining CeO2 nanoparticles (nanoceria) with better sensor performances than CeO2 nanoparticles synthesized by the solvothermal method. Metal–organic framework-derived nanoceria (MOFdNC) were functionalized with spheric gold nanoparticles (AuNPs) to further improve non-enzymatic electrode material for highly sensitive detection of prominent biocompound uric acid (UA) at this modified carbon paste electrode (MOFdNC/AuNPs&amp;CPE). X-ray powder diffraction (XRPD) and transmission electron microscopy (TEM) analysis were used for morphological structure characterization of the obtained nanostructures. Cyclic voltammetry and electrochemical impedance spectroscopy, both in an [Fe(CN)6]3−/4− redox system and uric acid standard solutions, were used for the characterization of material electrocatalytic performances, the selection of optimal electrode modifier, and the estimation of nature and kinetic parameters of the electrode process. Square-wave voltammetry (SWV) was chosen, and the optimal parameters of technique and experimental conditions were established for determining uric acid over MOFdNC/AuNPs&amp;CPE. Together with the development of the sensor, the detection procedure was optimized with the following analytical parameters: linear operating ranges of 0.05 to 1 µM and 1 to 50 µM and a detection limit of 0.011 µM, with outstanding repeatability, reproducibility, and stability of the sensor surface. Anti-interference experiments yielded a stable and nearly unchanged current response with negligible or no change in peak potential. After minor sample pretreatment, the proposed electrode was successfully applied for the quantification of UA in milk.

Ultra Enhancement of the Resolution of the Divergence Beam-Based SPR Sensor Using a Wavelet Filter

Surface Plasmon Resonance (SPR) sensors are highly sensitive to refractive index variations, making them ideal for biosensing and chemical detection applications. However, their performance can be constrained by noise, resolution limits, and signal distortions, particularly in divergence beam-based configurations. This research presents an ultra-enhancement of the performance of divergence beam-based SPR sensors by employing advanced wavelet filtering techniques. Wavelets, with their multi-resolution analysis capability, are applied to denoise and refine the sensor signal, significantly improving key performance metrics, including resolution, mean square error (MSE), root mean square error (RMSE), signal-to-noise ratio (SNR), sensitivity matrix (SM), and combined sensitivity factor (CSF).The wavelet filter effectively decomposes the SPR signal into distinct frequency bands, separating noise while retaining high-frequency components required for accurate sensing. This results in a significant reduction in MSE and RMSE of 30 and 92.26%, respectively, while simultaneously improving SNR by 54.08%, maintaining signal quality. The wavelet filter significantly improved the resolution of the SPR sensor by 99.95% (1.3772×10-7 RIU), allowing for more precise detection of refractive index changes and boosting diverge beam-based SPR sensor performance. In addition, a sensitivity matrix (SM) and combined sensitivity factor (CSF) were improved by 42 and 41.94%, respectively, allowing for a more thorough evaluation of the sensor's performance across multiple operational parameters. Simulation and experimentation show that wavelet filtering surpasses standard filtering approaches in terms of noise suppression and signal clarity. This technology has considerable potential for improving the accuracy and reliability of SPR sensors in high-sensitivity applications

Varying Performance of Low-Cost Sensors During Seasonal Smog Events in Moravian-Silesian Region

Air pollution monitoring in industrial regions like Moravia-Silesia faces challenges due to complex environmental conditions. Low-cost sensors offer a promising, cost-effective alternative for supplementing data from regulatory-grade air quality monitoring stations. This study evaluates the accuracy and reliability of a prototype node containing low-cost sensors for carbon monoxide (CO) and particulate matter (PM), specifically tailored for the local conditions of the Moravian-Silesian Region during winter and spring periods. An analysis of the reference data observed during the winter evaluation period showed a strong positive correlation between PM, CO, and NO2 concentrations, attributable to common pollution sources under low ambient temperature conditions and increased local heating activity. The Sensirion SPS30 sensor exhibited high linearity during the winter period but showed a systematic positive bias in PM10 readings during Polish smog episodes, likely due to fine particles from domestic heating. Conversely, during Saharan dust storm episodes, the sensor showed a negative bias, underestimating PM10 levels due to the prevalence of coarse particles. Calibration adjustments, based on the PM1/PM10 ratio derived from Alphasense OPC-N3 data, were initially explored to reduce these biases. For the first time, this study quantifies the influence of particle size distribution on the SPS30 sensor’s response during smog episodes of varying origin, under the given local and seasonal conditions. In addition to sensor evaluation, we analyzed the potential use of data from the Copernicus Atmospheric Monitoring Service (CAMS) as an alternative to increasing sensor complexity. Our findings suggest that, with appropriate calibration, selected low-cost sensors can provide reliable data for monitoring air pollution episodes in the Moravian-Silesian Region and may also be used for future adjustments of CAMS model predictions.