What does the guitalele like sound: Guitar or ukelele? An application of the K-means cluster method
This study embarks on a novel investigation into the guitalele’s acoustic properties, aiming to delineate its sound profile relative to its progenitors—the guitar and the ukulele. Leveraging a multifaceted approach that combines subjective perception surveys with objective frequency response analysis, we scrutinize the guitalele’s harmonic spectrum to elucidate its unique tonal identity. The experimental setup involved precise recordings of each instrument’s output across a range of notes, followed by sophisticated audio analysis techniques including Fourier transform to dissect the harmonic components and cross-correlation functions to identify representative sound pulses. In addition, advanced statistical methods, specifically K-means clustering, were applied to the harmonic data, offering a quantitative perspective on the guitalele’s sound classification relative to the guitar and ukulele. The investigation reveals that the guitalele embodies a complex acoustic blend, mirroring the guitar’s harmonic richness while retaining timbral characteristics reminiscent of the ukulele. Despite its closer visual and geometric alignment with the ukulele, the guitalele’s sound is predominantly influenced by the guitar, suggesting a hybrid sonic identity that transcends a simple binary classification. This unique amalgamation of sound properties suggests that the guitalele offers musicians a distinct voice that leverages the qualities of both instruments while establishing its own acoustic signature.
Multichannel Sensor for Detection of Molybdenum Ions Based on Nitrogen-Doped Carbon Quantum Dot Ensembles
Trace elements such as cobalt (Co), molybdenum (Mo), and zinc (Zn) play necessary roles in different biological functions. Co is a microelement that influences the vascular system. Mo works as an enzymatic cofactor of three enzymes (aldehyde oxidase, sulfite oxidase, and xanthine oxidase dehydrogenase). However, these elements are difficult to detect, since the analytical methods developed have a high cost, which restrict their applicability. In this sense, fluorescent sensors are an alternative for detecting trace elements, such as Mo4+ ions. Herein, a new multichannel trace elements sensor has been proposed to detect Mo entities. In this sense, two different N-CQDs were synthesized and fully characterized. The N-CQDs presented quantum yield values of 25.93% and 6.02% and excellent solubility in water. Also, a mixture of these two carbon-based nanoparticles was used to identify and to quantify Mo in water between seven different trace elements. The method was found to reach 1.28 and 3.88 ppm for limit of detection (LOD) and quantification (LOQ), respectively. To further verify the potential of the detection platform, the multichannel sensor was applied to identify the different concentrations of metal ions (Fe2+, Co2+, Mn2+, Cu2+, Zn2+, Mg2+, and Mo4+) in water. The data matrix was treated using different algorithms, such as K-Means and Discriminant Analysis (DA). The detection strategy has successfully identified the molybdenum ions at 5 ppm. This result shows the potential application of a multichannel sensor toward the detection of Mo entities, since it is comparable with the molybdenum test already available on the market.