Abstract: Advances on System-On-Chip and organic sensors allows the development of miniaturized impedance measurement hardware for gas monitoring in IoT. In this work, we present the development of miniaturized, multiplexed, and connected platform for impedance sp ectroscopy. Designed for online measurements and adapted to wireless network architectures, our platform has been tested and optimized to be used for multi-selective chemical organic sensor nodes. Our designed circuit is built from low cost and low power consumption microelectronics components providing real time acquisition. The proposed system is based on ESP32 Microcontroller enabling the management of an impedance network analyzer AD5933 (Analog Devices, Norwood, MA, USA) through its I2C interface. Our system benefits from two multiplexer components allowing calibration process and the interface of 15 conductimetric sensors with fast acquisition (less than 90 ms per acquisition). The paper describes the microelectronics design, the impedance respon se over time, the measurement's sensitivity and accuracy and the testing of the platform with embedded chemical sensors for gas classification and recognition.

Abstract: Identifying relevant machine-learning features for multi-sensing platforms is both an applicative limitation to recognize environments and a necessity to interpret the physical relevance of transducers' complementarity in their information processing. Pa rticularly for long acquisitions, feature extraction must be fully automatized without human intervention and resilient to perturbations without increasing significantly the computational cost of a classifier. In this study, we investigate the relative r esistance and current modulation of a 24-dimensional conductimetric electronic nose, which uses the exponential moving average as a floating reference in a low-cost information descriptor for environment recognition. In particular, we identified that dep ending on the structure of a linear classifier, the 'modema' descriptor is optimized for different material sensing elements' contributions to classify information patterns. The low-pass filtering optimization leads to opposite behaviors between unsuperv ised and supervised learning: the latter one favors longer integration of the reference, allowing to recognize five different classes over 90%, while the first one prefers using the latest events as its reference to cluster patterns by environment nature . Its electronic implementation shall greatly diminish the computational requirements of conductimetric electronic noses for on-board environment recognition without human supervision.

Abstract: Multivariate data analysis and machine-learning classification become popular tools to extract features without physical models for complex environments recognition. For electronic noses, time sampling over multiple sensing elements must be a fair compro mise between a period sufficiently long to output a meaningful information pattern, and sufficiently short to minimize training time for practical applications. Particularly when reactivity's kinetic differs from thermodynamic in sensitive materials, fin ding the best compromise to get the most from data is not obvious. Here, we investigate on the influence of data acquisition to improve or alter data clustering for molecular recognition on a conducting polymer electronic nose. We found out that waiting for sensing elements to reach their steady state is not required for classification, and that reducing data acquisition down to the first dynamical information suffice to recognize molecular gases by principal component analysis with the same materials. Especially for online inference, this study shows that a good sensing array is no array of good sensors, and that new figure-of-merits shall be defined for sensing hardware aiming machine-learning pattern-recognition rather than metrology.

Abstract: This study presents the development of an electronic nose comprising eight homemade sensors with pure P3HT and doped with different materials. The objective is to electronically identify the gases exposed on these sensors and evaluate the accuracy of tar get gas classification. The resistance variation for each sensor is measured over time and the collected data were processed by three different identification techniques as following: principal component analysis (PCA), linear discriminate analysis (LDA) , and nearest neighbor analysis (kNN). The merit factor for the analysis is the relative modulation of the resistance is very important and computationally gives different results. In addition, the fact that we have sensors made with innovative materials where the reproducibility of the response for the same material can be a constraint in the recognition. In contrast, we have shown that despite the lack of reproducibility for the same material on two different sensors and despite the instability during the ten last sec, we have good recognition rates and we can even say which algorithm is better. It is noted that the LDA is the most reliable and efficient method for gas classification with a prediction accuracy equal to 100%, whereas it reach 93.52% a nd 73.14% for PCA and kNN, respectively, for other techniques for 40% of training dataset and 60% of testing dataset.

Abstract: Surface acoustic waves (SAWs) have a broad spectrum of applications, especially in sensing. However, deposition methods of sensitive layers can be controlled locally through electrodeposition unlike other conventional methods, such as drop-casting and at mospheric-pressure plasma. Employing this method, we experimentally demonstrate the local electrodeposition of PEDOT: PSS on a structured active gold surface. We investigate the response of a piezoelectric transducer at 215MHz, by exploiting shear-horizo ntal (SH) surface waves of the ST-cut quartz substrate. The sensor responses were then tested under acetone, methanol, isopropanol and water vapor gases. Phase changes were more observed under water vapors gases. These changes depended on the surface con ductivity of PEDOT: PSS deposited on the sensor. The double-port SAW gas sensors modified with the PEDOT: PSS presented the highest sensitivity in the case of water vapor, with a maximum phase shift of 0.8º and an insertion loss of about 0.5 dB at ro om temperature. The obtained results could pave the way to implement advanced designs of high-performance and wireless electroacoustic gas sensors based on electropolymerization.

Abstract: In this work, we demonstrate P3HT (poly 3-hexylthiophene) organic rectifier diode both in rigid and flexible substrate with a rectification ratio up to 106. This performance has been achieved through tuning the work function of gold with a self-assembled monolayer of 2,3,4,5,6-pentafluorobenzenethiol (PFBT). The diode fabricated on flexible paper substrate shows a very good electrical stability under bending tests and the frequency response is estimated at more than 20 MHz which is sufficient for radio frequency identification (RFID) applications. It is also shown that the low operating voltage of this diode can be a real advantage for use in a rectenna for energy harvesting systems. Simulations of the diode structure show that it can be used at GSM an d Wi-Fi frequencies if the diode capacitance is reduced to a few pF and its series resistance to a few hundred ohms. Under these conditions, the DC voltages generated by the rectenna can reach a value up to 1 V.

Abstract: Organic semiconductors have enough molecular versatility to feature chemo-specific electrical sensitivity to large families of chemical substituents via different intermolecular bonding modes. This study demonstrates that one single conducting polymer ca n be tuned to either discriminate water-, ethanol- or acetone-vapors, on demand, by changing the nature of its dopant. Seven triflate salts differ from mild to strong p-dopant on poly(3-hexylthiophene) sensing micro-arrays. Each material shows a pattern of conductance modulation for the polymer which is reversible, reproducible, and distinctive of other gas exposures. Based on principal component analysis, an array doped with only two different triflates can be trained to reliably discriminate gases, wh ich re-motivates using conducting polymers as a class of materials for integrated electronic noses. More importantly, this method points out the existence of tripartite donor-acceptor charge-transfer complexes responsible for chemospecific molecular sens ing. By showing that molecular acceptors can have duality to p-dope semiconductors and to coordinate donor gases, such behavior can be used to understand the role of frontier orbital overlapping in organic semiconductors, the formation of charge-transfer complexes via Lewis acid-base adducts in molecular semiconductors.

Abstract: We have adapted a "peel-off" process to structure stacked organic semiconductors (conducting polymers or small molecules) and metal layers for diode microfabrication. The fabricated devices are organic diode rectifier in a coplanar waveguide structure. U nlike conventional lithographic process, this technique does not lead to destroy organic active layers since it does not involve harsh developer or any non-orthogonal solvent that alter the functionality of subsequentially deposited materials. This proce ss also involves recently reported materials, as a p-dopant of an organometallic electron-acceptor Copper (II) trifluoromethanesulfonate, that play the role of hole injection layer in order to enhance the performances of the diode. Comparatively to self- assembled monolayers based optimized structures, the fabricated diodes show higher reproducibility and stability. High rectification ratio for realized pentacene and poly (3-hexylthiophene) diodes up to 106 has been achieved. Their high frequency respons e has been evaluated by performing theoretical simulations. The results predict operating frequencies of 200 MHz and 50 MHz for pentacene and P3HT diode rectifiers respectively, with an input oscillating voltage of 2 V peak-to-peak, promising for RFID de vice applications or for GSM band energy harvesting in low-cost IoT objects.

Abstract: Simultaneously optimizing performances, processability and fabrication cost of organic electronic materials is the continual source of compromise hindering the development of disruptive applications. In this work, we identified a strategy to achieve reco rd conductivity values of one of the most benchmarked semiconducting polymers by doping with an entirely solution-processed, water-free and cost-effective technique. High electrical conductivity for poly(3-hexylthiophene) up to 21 S/cm has been achieved, using a commercially available electron acceptor as both a Lewis acid and an oxidizing agent. While we managed water-free solution-processing a three-time higher conductivity for P3HT with a very affordable/available chemical, near-field microscopy reve als the existence of concentration-dependent higher-conductivity micro-domains for which furthermore process optimization might access to even higher performances. In the perpetual quest of reaching higher performances for organic electronics, this work shall greatly unlock applications maturation requiring higher-scale processability and lower fabrication costs concomitant of higher performances and new functionalities, in the current context where understanding the doping mechanism of such class of ma terials remains of the greatest interest.