Sensor Technique Research Articles

Flexible Electronics: Advancements and Applications of Flexible Piezoelectric Composites in Modern Sensing Technologies.

The piezoelectric effect refers to a physical phenomenon where piezoelectric materials generate an electric field when subjected to mechanical stress or undergo mechanical deformation when subjected to an external electric field. This principle underlies the operation of piezoelectric sensors. Piezoelectric sensors have garnered significant attention due to their excellent self-powering capability, rapid response speed, and high sensitivity. With the rapid development of sensor techniques achieving high precision, increased mechanical flexibility, and miniaturization, a range of flexible electronic products have emerged. As the core constituents of piezoelectric sensors, flexible piezoelectric composite materials are commonly used due to their unique advantages, including high conformability, sensitivity, and compatibility. They have found applications in diverse domains such as underwater detection, electronic skin sensing, wearable sensors, targeted therapy, and ultrasound diagnostics for deep tissue. The advent of flexible piezoelectric composite materials has revolutionized the design concepts and application scenarios of traditional piezoelectric materials, playing a crucial role in the development of next-generation flexible electronic products. This paper reviews the research progress on flexible piezoelectric composite materials, covering their types and typical fabrication techniques, as well as their applications across various fields. Finally, a summary and outlook on the existing issues and future development of these composite materials are provided.

Thiram Determination in Milk Samples by Surface Plasmon Resonance Based on Molecularly Imprinted Polymers and Sulphur-Doped Titanium Dioxide.

In this work, a new surface plasmon resonance (SPR) sensor based on sulphur-doped titanium dioxide (S-TiO2) nanostructures and molecularly imprinted polymer (MIP) was presented for thiram (THI) determination in milk samples. Firstly, the S-TiO2 nanomaterial with a high product yield was prepared by using a facile sol-gel hydrolysis technique with a high product yield. After that, UV polymerization was carried out for the preparation of the THI-imprinted SPR chip based on S-TiO2 using a mixture including ethylene glycol dimethacrylate (EGDMA) as the cross-linker, N,N'-azobisisobutyronitrile (AIBN) as the initiator, and methacryloylamidoglutamicacid (MAGA) as the monomer. The reliability of the sensor preparation procedure has been successfully proven by characterization studies of the prepared nanomaterials and SPR chip surfaces through spectroscopic, microscopic, and electrochemical methods. As a result, the prepared SPR sensor showed linearity in the range of 1.0 × 10-9-1.0 × 10-7 M with a detection limit (LOD) of 3.3 × 10-10 M in the real samples, and a sensor technique for THI determination with high sensitivity, repeatability, and selectivity can be included in the literature.

Advancing Wireless Strain Sensing with Low-cost Printable Supersensitive Radiofrequency Patches

Engineering structures experience changes in material and functional properties due to structural damage, induced by sudden events or gradual degradation over time. Structural Health Monitoring (SHM) is crucial, utilizing real-time monitoring systems with various sensors. Suitable sensors for monitoring structures are vital for future economies. They should feature sensitivity, cost-effectiveness, easy integration, and reduced complexity. Current strain sensors, like strain gauges and Fiber-optic Brag Gratings (FBG), are sensitive but have drawbacks, including wiring complexities, installation time, and limited lifetimes. Introducing wireless sensors as a solution, enables autonomous strain monitoring, overcoming the drawbacks of traditional wired sensors. Chipless Radio Frequency (RF) technology, specifically passive RF sensors, stands out for smart structure development. These sensors, relying on RF readers for power, offer compactness, non-intrusiveness, longer lifetimes, and cost-effectiveness, making them promising for monitoring large structures. This research aims to develop an innovative chipless RF strain sensor for wireless communication, emphasizing enhanced sensitivity and seamless integration into real structures for integrity monitoring. First, the structure and processing technique of such sensors will be presented. Second, an external readout coil connected to a vector network analyzer was used to make electromagnetic coupling with the receiver coil in the sensor and resonate the sensor on its resonant frequency. When experiencing strain, the signal activates a transmission line mechanism, increasing the attenuation along the capacitive element. The consequence is a strong variation in the effective sensor length making a remarkable variation in the resonant frequency. We demonstrate the exceptional gauge factor in the resonant frequency at different strain levels, which gives our sensor a big advantage, compared to pure resistive sensors and geometrically related capacitive sensors. We also demonstrate that such sensors can be fabricated in large quantities at very low cost as they are by design compatible with classical printing technologies.

Water-Assisted High-Yield Dicing Technique for MEMS Pressure Sensors with Ultra-Thin Structures

Water-Assisted High-Yield Dicing Technique for MEMS Pressure Sensors with Ultra-Thin Structures

Structural Health Monitoring of the Roman bridge S. Angelo on the Savuto river (Scigliano, Italy), by integration of a new sensor and geomatic techniques

The Roman Bridge of S. Angelo, located along the road that connected Capua to Reggio Calabria (Via ab Regio ad Capuam), constitutes an important remain of Roman engineering works and is the best-preserved bridge along the road itself. Located in the middle valley of the Savuto river, this bridge is currently subject to structural monitoring by using various integrated techniques. The monitoring is carried out as part of a series of initiatives aimed at gaining in-depth knowledge and safeguarding a series of historic buildings in the same area. A new type of sensor, characterized by features not offered at the same time by those currently on the market, has been developed to aid structural and environmental monitoring. Previous surveys had been carried out with geomatic techniques; one of the products of these surveys is a 3D model of the bridge, used as input to carry out a Finite Element Modeling (FEM) analysis. The results of this FEM analysis will be compared with those obtained by the measurements carried out with the new sensor. The monitoring campaign, which has just begun, and the expected results are described and commented on. The main results obtained to date are the model of the bridge, a structural analysis and the settlements that have occurred over time as well as the creation of the new sensor.

LidPose: Real-Time 3D Human Pose Estimation in Sparse Lidar Point Clouds with Non-Repetitive Circular Scanning Pattern.

In this paper, we propose a novel, vision-transformer-based end-to-end pose estimation method, LidPose, for real-time human skeleton estimation in non-repetitive circular scanning (NRCS) lidar point clouds. Building on the ViTPose architecture, we introduce novel adaptations to address the unique properties of NRCS lidars, namely, the sparsity and unusual rosetta-like scanning pattern. The proposed method addresses a common issue of NRCS lidar-based perception, namely, the sparsity of the measurement, which needs balancing between the spatial and temporal resolution of the recorded data for efficient analysis of various phenomena. LidPose utilizes foreground and background segmentation techniques for the NRCS lidar sensor to select a region of interest (RoI), making LidPose a complete end-to-end approach to moving pedestrian detection and skeleton fitting from raw NRCS lidar measurement sequences captured by a static sensor for surveillance scenarios. To evaluate the method, we have created a novel, real-world, multi-modal dataset, containing camera images and lidar point clouds from a Livox Avia sensor, with annotated 2D and 3D human skeleton ground truth.

An evolutionary approach to dissolved oxygen mathematical modeling: A case study of the Klamath River

Aquaculture has emerged as a crucial sector in many countries. In the Recirculating Aquaculture System (RAS), Dissolved Oxygen (DO) levels are critical to the health of aquatic animals. As DO sensors are costly, a number of studies have proposed a soft sensor technique utilizing machine learning for estimating DO levels in water. However, the existing research work mainly focuses on black-box approaches, which do not provide numerical analysis between the DO levels and the related parameters. To solve this issue, a sequential Genetic Programming (GP) approach with an evolutionary refinement method is proposed to generate a mathematical expression that represents DO levels in water. In particular, a coarse mathematical model is generated using GP and subsequently fine-tuned using the Covariance Matrix Adaptation Evolution Strategy (CMA-ES). As a study case, the Klamath River dataset is used to generate the model. The evaluation of our proposed method uses datasets from both the Klamath River and the Fanno Creek. Two models are generated in this paper; one model uses six features, while the other only employs three. The results indicate that the model with six features exhibits relatively higher accuracy. However, it is worth noting that a smaller dataset of features is also capable of achieving generalization of the model.

Study on the hydrodynamics of structured packing: Liquid holdup and pressure drop of a novel 3D printed packing

SpiroPak is a patented innovative structured packing and can be used in process industries for chemical separation purposes. The core of its design is to ensure an uninterrupted flow path for both liquids and gases, leading to remarkably low-pressure drops but with substantial surface areas for efficient mass transfer. Experimental studies using the wire-mesh sensor technique have showcased SpiroPak's ability to eliminate the channelling effect that is observed in commercial packings. Furthermore, its wide-ranging liquid holdup capability encompasses the functionalities of conventional packings, offering clients the feasibility of diverse applications. In this study, the pressure-drop correlations for SpiroPak in this work are based on Stichlmair empirical formulas, with the coefficients of C1, C2 and C3 tuned through the curve fitting method. The primary objective of this study was to establish packing selection criteria based on the key hydraulic performance metrics, such as pressure drop and liquid holdup. By swiftly identifying suitable packing solutions, the method accelerates the pre-selection process, significantly reducing computational complexity and time expenditures at the early stages of research and development.

A self-adaptive correlated multiple sampling technique based on single-slope ADC for low-noise CMOS image sensors

This paper presents a self-adaptive correlated multiple sampling (SACMS) technique for low-noise CMOS image sensors. Compared to the traditional correlated multiple sampling (TCMS), SACMS technique adds a pre-determination stage to determine the brightness of the pixel, reducing the 4 large-range ramps to 3 small-range ramps and 1 large-range ramp when quantizing the signal. For dark pixels, the signal is quantized using small-range ramps with short sampling interval to reduce low-frequency noise. For bright pixels, the CMS operation is turned off and the signal is quantized only once in the large-range ramp to improve the frame rate. Simulation results demonstrate that, for dark pixels, SACMS achieves a minimum readout noise of 84.22μVrms at eight times analog gain, compared to 108.76μVrms with TCMS, representing an approximately 21.7 % improvement in noise suppression. Additionally, the sampling rate of the SSADC employing SACMS technique is 36.36 kHz, while the sampling rate of the SSADC using TCMS technique is 21 kHz.

Non-invasive Blood Glucose Monitoring Using a Radiofrequency Sensor and a Machine Learning Model

Diabetes Mellitus (DM) affects over 530 million people globally and is a condition characterized by high blood glucose (BG) that can result in severe long-term health consequences if poorly managed. To mitigate the risks associated with DM, it is crucial to regulate BG levels through regular monitoring. Current methods of monitoring BG come with drawbacks in the form of pain and expense. With the rapid increase in prevalence of DM, there is an increasing need for economical, accurate, and non-invasive continuous measures of BG. Here we present a validation for a novel sensor that rapidly scans through a wide range of radio frequencies (RF) and uses machine learning (ML) techniques for the purpose of non-invasive BG measurement. After model training, the RF sensor and ML techniques employed in this study can predict BG at a level significantly better than chance, as shown below. Data were collected from 13 healthy participants over a series of tests lasting 2 – 3 hours. During each test, participants placed their forearm on an RF sensor that measured their BG levels using sweeps across the 500 MHz – 1500 MHz range at 0.1 MHz intervals. Participants ingested 37.5 grams of glucose solution to generate BG readings across normoglycemic and hyperglycemic ranges. Each sweep took approximately 22 seconds, including a one second pause between sweeps. Across 110 tests, we collected 3,311 BG observations. Concurrent measurements from a Continuous Glucose Monitor (CGM) were taken as reference. Data were divided using a 60-20-20 (training-validation-test) split and trained on a Light Gradient-Boosting Machine (lightGBM) model to predict BG values. A ‘blind’ evaluation of model performance was determined using the held-out test dataset. We predicted BG values in the held-out test dataset with a Mean Absolute Relative Difference (MARD) of 10.8% in the normoglycemic range and 15.9% in the hyperglycemic range (11.27% overall MARD). These results were significantly better than a chance model (two-sample t-test, p <0.01). In a Surveillance Error Grid analysis of model accuracy, 89.0% of predictions fell in Risk Level 0, 10.1% in Risk Level 1, and 0.9% in Risk Level 2. These results demonstrate that the novel RF sensor and ML techniques described can predict a reference BG value in the population under study. More research is underway to refine and expand these methods using a gold-standard blood glucose reference device and among a broader participant population with an expanded glucose range. DK and VS are consultants for and own stock in Know Labs. JA and KC are employed by and have stock options in Know Labs. CW and KP are consultants for Know Labs. NA. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Calibration technique for airborne vibration sensors based on actuators

Addressing the challenge of in-situ calibration for onboard vibration sensors in aircraft installations, we propose an online calibration method based on actuators. Leveraging the inverse piezoelectric effect, we employ piezoelectric actuators mounted on the vibration sensors to calibrate vibration signals. Experimental results demonstrate that the influence of the actuator on the energy transfer of the measured sensor’s vibration signal during the calibration process can be negligible, meeting the calibration accuracy requirements of onboard vibration sensors. Through validation experiments, we determine the excitation coefficients of the actuators at different frequencies, subsequently obtaining the sensitivity of the vibration sensors equipped with actuators. The results reveal that within the frequency range, the relative error of sensitivity remains within ±3%, satisfying the relevant calibration requirements. Therefore, the actuator-based online calibration technique offers an efficient means of calibrating onboard vibration sensors, carrying significant practical value.

Adaptive Filtering Noise Suppression Technique for EFA-Based Interferometric Fiber Optic Sensors

Adaptive Filtering Noise Suppression Technique for EFA-Based Interferometric Fiber Optic Sensors

Evaluation of the Agreement Between a New Pyramid Wavefront Sensor Aberrometer and Scheiner-Smirnov Aberrometers.

To assess agreement between a new aberrometer (Osiris-T; CSO) employing pyramid wavefront sensor technique and Scheiner-Smirnov aberrometer (OPD-Scan III; Nidek) on measuring ocular, corneal, and internal aberrations in healthy participants. The measurements were conducted three times consecutively by an experienced examiner. The total root mean square (RMS) aberrations, higher order aberration RMS, coma Z3±1, trefoil Z3±3, spherical aberration Z40, and astigmatism II Z4±2 up to 7th order were exported in both 4-and 6-mm pupil zones. The parameters between the two devices were statistically compared using the paired t-test, and the differences assessed with Bland-Altman plots and 95% limits of agreement. This prospective study included 70 right eyes of 70 healthy participants with an average age of 25.94 ± 6.59 years (range: 18 to 47 years). The mean difference in the two devices ranged from 0.01 µm for astigmatism II Z4±2 to 0.63 µm for total RMS in 4 mm and from 0.01 to 1.41 µm in 6-mm pupil size. The Bland-Altman analysis of ocular, corneal, and internal aberrations indicated high agreement between the two devices and the maximum absolute values for 95% limits of agreement ranged from 0.03 to 1.06 µm for 4-mm pupil diameters and 0.12 to 1.13 µm for 6-mm pupil diameters. The newly developed pyramid wavefront sensor technique aberrometer demonstrated a high agreement with a Scheiner-Smirnov aberrometer when measuring ocular, corneal, and internal aberrations in healthy participants. Thus, the two aberrometers may be considered interchangeable for clinical applications. [J Refract Surg. 2024;40(4):e218-e228.].

Estimating Near-Surface Soil Hydraulic Properties through Sensor-Based Soil Infiltrability Measurements and Inverse Modeling

Near-surface soil hydraulic properties (SHPs) are fundamental for describing and predicting water and energy exchange processes, particularly at the soil–atmosphere interface, and regulating evapotranspiration, infiltration, and runoff in different ecosystems. In this study, a new method was proposed to estimate near-surface SHPs by combining sensor-based soil infiltrability measurements with inverse modeling using HYDRUS-2D. The infiltration rate (IR) was estimated by combining the linear source inflow method with image processing, and Ks was estimated from the near-surface steady-state IR (NSIRM). The SWRC parameters described by the van Genuchten model were estimated using the inverse modeling method of HYDRUS-2D for the fitting of sensor-measured infiltration data. Subsequently, the parameters of the van Genuchten model, including α, n, and l, were inversely estimated. Three undisturbed soils, including two stand humus samples from cork oak (Quercus suber L.) and oleander (Pinus tabuliformis L.) stands and one sandy loam from a farmland, were sampled near the soil surface to validate the proposed method. The estimated Ks was evaluated by the constant head method (CHM). The estimated parameters of the SWRC were validated by those determined through the simultaneous measurement of the soil moisture content and water potential using sensor techniques. The results showed that the Ks estimated from the NSIRM for each soil sample were 23.40 ± 1.21, 23.86 ± 1.83, and 22.99 ± 2.26 mm h−1, respectively. In comparison, the Ks determined by the CHM were 24.41 ± 1.53, 24.26 ± 0.37, and 23.81 ± 0.10 mm/h, respectively. The relative errors of the proposed method were 4.14%, 1.64%, and 3.42%, respectively. For the SWRC estimation, the normalized root mean square errors (NRMSEs) between the measurements and the estimates for each soil sample were 0.1724, 0.1454, and 0.0606, respectively. Based on this, the AWC was obtained, and Ku was deduced from the estimated Ks and SWRC parameters for each soil sample. In general, the proposed method successfully estimates near-surface SHPs, simplifies the measurement device, and provides a new perspective for the in situ determination of near-surface SHPs under field conditions in the near future.

A combined technique of implantable sensors and probabilistic localization method for monitoring acoustic events on concrete slab

Continuous monitoring of acoustic events in concrete such as cracking and impacting, and providing their location information is a significant topic in structural health monitoring (SHM). This paper develops a combined technique of concrete implantable cube (CIC) and probabilistic localization method to monitor acoustic sources on concrete slabs. The CIC that integrates an octahedron-shaped piezoelectric sensor array can be implanted in concrete structures in long term and supply an omni-dimensional sensing capacity. Aiming at the designed sensor array and the impacts of randomness and heterogeneity of concrete material on the localization accuracy, a probabilistic localization method was proposed. To demonstrate the feasibility of the proposed technique, an experiment including 48 impact tests was conducted on a concrete slab specimen. The acoustic signals received by the implanted CIC were used to reconstruct localization maps to predict the impact areas. The results were quantified using three matrixes including accuracy, precision, and recall; consequently, the accuracy obtained the highest average score close to 0.98 and performed most steadily. In addition, to optimize the proposed technique furtherly, the sensitivity of two key parameters associated with this method were discussed. Overall, the experimental study showed a great potential of this developed technique for long-term monitoring acoustic events on concrete structures.

Enhancing pathogen identification in cheese with high background microflora using an artificial neural network-enabled paper chromogenic array sensor approach

Biohazards, which may occur at all supply chain stages, pose significant threats to food safety and public health. Addressing these concerns and enhancing food safety necessitates a nondestructive pathogen surveillance approach capable of continuously and simultaneously detecting multiple pathogens. Detecting and differentiating low concentrations of pathogenic bacteria amid high background microflora levels in foods is challenging, requiring technology with high sensitivity and robust discriminatory capability. This study introduces an artificial neural network-driven paper chromogenic array sensor (ANN-PCA) technique developed for the nondestructive, continuous, and simultaneous detection of Salmonella Enteritidis (SE) and Escherichia coli O157:H7 (Ec) from a high background microflora in shredded cheddar cheese. This method enables accurate detection of SE and Ec in monoculture and cocktail culture while distinguishing them from a high level of background microflora (∼7.5 log CFU/g), with accuracies ranging from 72 ± 11% to 92 ± 3%. In addition, SE and Ec were successfully identified at concentrations as low as 1 log CFU/g within one day, with an accuracy of 72 ± 11%. This approach exhibits promising potential for integration into a digitalized, smart, and resilient nondestructive surveillance system for real-time pathogen detection in foods throughout the supply chain without enrichment, incubation, or other sample preparation steps.

A comprehensive study of a long-term creep thermo-mechanical fatigue behavior monitoring of BFRP composite pipeline using electrical capacitance sensors and deep learning algorithm

The composite pipeline is a relatively new and viable alternative pipeline to the more commonly used traditional one due to its good mechanical and fatigue properties and lower production cost. For this purpose, it is critical to assess the mechanical and fatigue performance of composite pipeline material under various working conditions, particularly for monitoring long-term creep thermo-mechanical fatigue behavior. In this paper, a long-term creep thermo-mechanical fatigue behavior in a basalt fiber reinforced polymer laminated composite pipeline is detected through an integrated expert system consisting of the electrical capacitance sensors and a deep learning algorithm. First, a multi-physics finite element model is established for the simulation of a long-term creep thermo-mechanical fatigue behavior in basalt fiber reinforced polymer composite pipelines subjected to long-term fatigue loading of internal pressure and thermal effect. Second, theoretical model results of long-term creep thermo-mechanical fatigue compliance (Sf(t)) over the time of creep are analyzed in pipeline material using the modulus degradation approach. Finally, an electrical potential change between electrical capacitance sensors electrodes corresponding to Sf(t) over the time of creep for some levels of long-term creep thermo-mechanical fatigue (Rf%) is recorded and then used in these datasets for training of the novel deep neural network based on one of the most widely used of the deep neural network families is the convolutional neural network, to predict Sf(t) in pipeline for various Rf% not included in the previous finite element model evaluation (i.e. electrical capacitance sensors technique). In this paper first is detected the long-term creep thermo-mechanical fatigue behavior for Rf%=25%,50%, and 75% from a finite element model and modulus degradation approach, and then is predicted the long-term creep thermo-mechanical fatigue behavior for Rf%=15%,35%,60%, and 85% respectively via deep neural network. The proposed method results are in good agreement with the experimental results available in the literature, thus verifying the accuracy and reliability of the proposed technique and its applicability to other different composite structures.

Long-term biochar application influences phosphorus and associated iron and sulfur transformations in the rhizosphere

The effects of long-term biochar application on soil phosphorus (P) flux across the root-soil interface and its availability in the rhizosphere of rice (Oryza sativa L) remain unclear. We used diffusive gradients in thin films (DGT), laser ablation-inductively coupled plasma mass spectrometry, and planar optode sensor techniques to characterize, in-situ, the 2D heterogeneity and dynamics of rhizosphere soil P, iron (Fe), sulfur (S) and trace element fluxes, dissolved oxygen and pH in paddy soil, after 10 years of biochar application. Compared to the control (no biochar applied), biochar applied at 4.5, 22.5 and 45.0 Mg ha−1 yr−1 decreased rhizospheric P fluxes by 11.6%, 63.4% and 79.0%, respectively. This decrease under biochar treatments was attributed to changed redox status of Fe and S caused by the lower dissolved oxygen in rhizosphere soil and increased soil pH induced precipitating of soluble inorganic P into insoluble P forms, such as calcium-bound and residual P that are unavailable for crop uptake. Higher application rate of biochar resulted in lower As and Pb fluxes in rice rhizosphere and their availabilities for crop uptake. The in-situ observation results in rice rhizosphere at μm-scale after 10 years of biochar addition directly showed the complex effects of long-term biochar and rhizosphere heterogeneity on P transformation process.Graphical

Purpald-functionalized biosensor for simultaneous electrochemical detection of ascorbic acid, uric acid, L-cysteine and lipoic acid

The polymer purpald (4-amino-3-hydrazino-5-mercapto-1,2,4-triazole) film on glassy carbon electrode (p-Purpald@GCE) was fabricated using purpald monomer by electropolymerization method, and the modified electrode was applied as a probe for the simultaneous sensing of ascorbic acid (AA), uric acid (UA), L-cysteine (L-Cys) and lipoic acid (LA). The modified p-Purpald@GCE was well characterized by Scanning electrode microscopy (SEM), X-ray photoelectron spectroscopy (XPS), Cyclic Voltammetry (CV), Electrochemical impedance spectroscopy (EIS) and Differential Pulse Voltammetry (DPV) techniques. CV, EIS results were confirmed the good electrocatayst nature of p-Purpald@GCE. Independent and simultaneous detection of AA, UA, L-Cys, and LA ware performed by DPV method. For Independent detection, based on the oxidation current changes, we have calculated the limit of detection (LOD) of 392, 137, 341 and 200 nM (LOD) = 3S/m) for AA, UA, L-Cys and LA, respectively. The simultaneous detection of AA, UA, L-Cys, and LA executed and LOD found to be 364, 132, 275 and 192 nM, respectively. The modified electrode shows the high selectivity towards AA, UA, L-Cys and LA even in the presence of high concentration of other interferences. The developed sensor technique will be useful for the sensitive and simultaneous detection of AA, UA, L-Cys, and LA in food and clinical samples.

Exploring the effect of porphyrin chemical structure on the performance of polymer-based Pressure-Sensitive Paints

Pressure sensitive paints (PSPs) are a powerful optical oxygen sensor technique for measuring the surface pressure distribution upon an aircraft model in wind tunnel testing. Traditional PSPs have relied heavily on platinum(II)-5,10,15,20-tetrakis-(2,3,4,5,6-pentafluorphenyl)-porphyrin due to its reasonable quantum yield of emission, high pressure sensitivity, resistance to photo oxidation, and commercial availability. This work investigates the effect of luminophore chemical structure on PSP performance by altering the degree, substitution pattern, and nature of halogen atom on the phenyl groups of ten different Pt(II) tetraphenyl porphyrins. From this initial screening it was found that platinum(II)-5,10,15,20-tetrakis-(3,5-bis(trifluoromethyl)phenyl)-porphyrin polymer PSPs had a substantially lower temperature sensitivity and a slightly higher pressure sensitivity. Using this new luminophore a Fluoro/Isopropyl/Butyl (FIB) polymer based PSP formulation was optimised whereby it was found that 3.2% w/v of FIB polymer and 800 μM concentration of platinum(II)-5,10,15,20-tetrakis-(3,5-bis(trifluoromethyl)phenyl)-porphyrin gave a low temperature sensitivity at 100 kPa for a single component polymer based PSP of 0.22%/K and a relatively high pressure sensitivity at 293 K of 0.846, exceeding current commercial PSP performance.