A Review on Metal Ion Sensors Derived from Chalcone Precursor

Disclosure of new molecular probes as chromogenic and fluorogenic cation sensors is scientifically exigent work. Recently chalcone derivatives gained more attention because of their structural variability. A suitable donor and acceptor groups separated by delocalized π-orbitals display excellent chromogenic and fluorogenic properties because of intramolecular charge transfer (ICT). These designed molecular frameworks provide the coordination sites to the incoming metal ions results in small changes in the optical properties. In a typical sensing behavior, coordination leads to a large conjugation plane with the probe resulted in hypo/hyperchromic shifts or red/blue shifts. In this review, we tried to converge the reported chalcone-derived sensors and explored the design, synthesis, metal ion sensing mechanism, and practical application of the probes. We expect that this review gives a basic outline for researchers to explore the field of chalcone-based sensors further.

A Comprehensive Review on Thiophene Based Chemosensors

The recognition and sensing of various analytes in aqueous and biological systems by using fluorometric or colorimetric chemosensors possessing high selectivity and sensitivity, low cost has gained enormous attention. Furthermore, thiophene derivatives possess exceptional photophysical properties compared to other heterocycles, and therefore they can be employed in chemosensors for analyte detection. In this review, we have tried to explore the design and detection mechanism of various thiophene-based probes, practical applicability, and their advanced models (design guides), which could be thoughtful for the synthesis of new thiophene-based probes. This review provides an insight into the reported chemosensors (2008-2020) for thiophene scaffold as effective emission and absorption-based chemosensors.

Gaussian Mixture Model Based Classification of Stuttering Dysfluencies

The classification of dysfluencies is one of the important steps in objective measurement of stuttering disorder. In this work, the focus is on investigating the applicability of automatic speaker recognition (ASR) method for stuttering dysfluency recognition. The system designed for this particular task relies on the Gaussian mixture model (GMM), which is the most widely used probabilistic modeling technique in ASR. The GMM parameters are estimated from Mel frequency cepstral coefficients (MFCCs). This statistical speaker-modeling technique represents the fundamental characteristic sounds of speech signal. Using this model, we build a dysfluency recognizer that is capable of recognizing dysfluencies irrespective of a person as well as what is being said. The performance of the system is evaluated for different types of dysfluencies such as syllable repetition, word repetition, prolongation, and interjection using speech samples from the University College London Archive of Stuttered Speech (UCLASS).