This paper presents a new pattern recognition approach for enhancing the selectivity of gas sensor arrays for clustering intelligent odor detection. The aim of this approach was to accurately classify an odor using pattern recognition in order to enhance the selectivity of gas sensor arrays. This was achieved using an odor monitoring system with a newly developed neural-genetic classification algorithm (NGCA). The system shows the enhancement in the sensitivity of the detected gas. Experiments showed that the proposed NGCA delivered better performance than the previous genetic algorithm (GA) and artificial neural networks (ANN) methods. We also used PCA for data visualization. Our proposed system can enhance the reproducibility, reliability, and selectivity of odor sensor output, so it is expected to be applicable to diverse environmental problems including air pollution, and monitor the air quality of clean-air required buildings such as a kindergartens and hospitals.

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Ferrous monosulfide mackinawite (FeS) forming under iron-rich sulfate-reducing conditions is an effective scavenger of heavy metals and metalloids. In this work, we studied the mechanisms of cadmium­(II) [Cd­(II)] (a toxic chalcophile metal generally considered redox-inactive) reactions with FeS under an anoxic condition over ranges of geochemical variables. The batch uptake experiments show that dissolved Cd­(II) is almost completely removed from the FeS suspensions without a distinct pH or ionic strength dependency under all the experimental conditions studied. The batch uptake results do not support (Cd,Fe)S solid-solution formation. Instead, identification of the solid-phase reaction products by a suite of surface chemical probes suggests that the major Cd reaction product is CdS, with minor amounts of metallic Cd (Cd0) and surface-complexed Cd­(II). Density functional theory calculations support the favorability of pure CdS formation compared to Fe-doped CdS or Cd-doped FeS. This study shows that sulfide precipitation largely controls the mobility of Cd, similar to previously reported Hg and As, implicating the presence of dissolved or solid-phase sulfides to be a key parameter in sulfate-reducing natural and engineered environments.

A biosensor is composed of a bioreceptor, an associated recognition molecule, and a signal transducer that can selectively detect target substances for analysis. DNA based biosensors utilize receptor molecules that allow hybridization with the target analyte. However, most DNA biosensor research uses oligonucleotides as the target analytes and does not address the potential problems of real samples. The identification of recognition molecules suitable for real target analyte samples is an important step towards further development of DNA biosensors. This study examines the characteristics of DNA used as bioreceptors and proposes a hybrid evolution-based DNA sequence generating algorithm, based on DNA computing, to identify suitable DNA bioreceptor recognition molecules for stable hybridization with real target substances. The Traveling Salesman Problem (TSP) approach is applied in the proposed algorithm to evaluate the safety and fitness of the generated DNA sequences. This approach improves efficiency and stability for enhanced and variable-length DNA sequence generation and allows extension to generation of variable-length DNA sequences with diverse receptor recognition requirements.