{'fraction_non_alphanumeric': 0.033192090395480225, 'fraction_numerical': 0.007768361581920904, 'mean_word_length': 5.160869565217391, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A Novel Toxic Gases Detection System Based on SAW Resonator Array and Probabilistic Neural Network

Surface acoustic wave (SAW) resonator array as the key element for analytical sensor system is a very promising technique for toxic gases detection. In this work, a novel analytical system based on high Q-value surface acoustic wave (SAW) resonator array and probabilistic neural network (PNN) was developed to detect toxic gases such as chemical warfare agents or the simulant. The array consisted of four two-port SAW resonator sensors with a fundamental frequency at 200 MHz was fabricated. To improve the selectivity and sensitivity, four polymers such as polyepichlorohydrin (PECH), Silicone (SE-30), Hexafluoro-2-propanol bisphenol-substituted siloxane polymer (BSP3), fluorinated polymethyldrosiloxane (PTFP), were selected as the sensitive film materials and were coated on the surface of different resonators of the array by spin-coating method respectively. Then, the array was used to detect mustard gas (HD), dimethyl methylphosphonate (DMMP), sarin (GB) and sarin acid. The frequency output of each sensor was mixed with a bare reference device. The signals obtained from the array were analyzed with PNN to identify the toxic gases. The success rate of identification was 90.87%. It showed that the SAW resonator array combined with PNN was able to detect and identify the above toxic gases quite well.