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 and 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.

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. Particularly for long acquisitions, feature extraction must be fully automatized without human intervention and resilient to perturbations without significantly increasing the computational cost of a classifier. In this study, we investigate the relative resistance 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 depending 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 unsupervised and supervised learning: the latter favors longer integration of the reference, allowing the recognition of 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.

Multivariate data analysis and machine learning classification have 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 compromise between a period sufficiently long to output a meaningful information pattern and sufficiently short to minimize training time for practical applications. Particularly when a reactivity’s kinetics differ from the thermodynamics in sensitive materials, finding the best compromise to get the most from the data is not obvious. Here, we investigate 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 suffices 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 not an array of good sensors, and that new figures of merit should be defined for sensing hardware using machine learning pattern recognition rather than metrology.