Advances in Near-Infrared Spectroscopy and Related Computational Methods

Beć, Krzysztof B.; Huck, Christian W.

doi:10.3390/molecules24234370

Cited by 15 publications

(7 citation statements)

References 35 publications

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“…The splitting reaction requires semiconductor materials such as oxides, nitrides, and sulfides [8,9]. Nevertheless, semiconductor polymers are the new category of photocatalytic materials used for this purpose having many advantages such as low cost, high quantum yield, large surface area, and easy preparation [10][11][12][13][14][15][16]. Polymer production is the replaceable category for all metal sources, especially in electronic devices.…”

Section: Introductionmentioning

confidence: 99%

ATO/Polyaniline/PbS Nanocomposite as Highly Efficient Photoelectrode for Hydrogen Production from Wastewater with Theoretical Study for the Water Splitting

Khalafalla

Hadia

Elsayed

et al. 2022

Adsorption Science & Technology

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Polyaniline-assisted deposition of PbS is carried out on antimony tin oxide (ATO) glass for ATO/PANI/PbS composite formation. The deposition of PbS was carried out inside and outside the polymer chains using the ionic adsorption deposition process. Various analyses were conducted to confirm the chemical structure and morphological, optical, and electrical properties of the resulting composite. TEM and SEM analyses demonstrated the spherical shape of PbS particles inside and outside the PANI network with more dark or white color, respectively. Moreover, the ImageJ program confirmed the composite formation. The XRD characterization showed the shifts in the PANI peaks after the composite formation with the appearance of a new additional peak related to PbS nanoparticles. The optical analyses were massively enhanced after the composite formation with more broadening in the Vis region at 630 nm, in which there was more enhancement in the bandgap that reached 1.5 eV. The electrode application in the H2 generation process was carried out from wastewater (sewage water, third treatment) without any additional sacrificing agent. The electrode responded well to light, where the current density ( J ph ) changed from 10-6 to 0.13 mA.cm-2 under dark and light, respectively. The electrode had high reproducibility and stability. The numbers of generated H2 moles were 0.1 mmol/cm2.h. The produced Δ H ∗ and Δ S ∗ were 7.3 kJ/mol and 273.4 J/mol.K, respectively. Finally, the mechanism explains the H2 generation reaction using three-electrode cell.

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Section: Introductionmentioning

confidence: 99%

ATO/Polyaniline/PbS Nanocomposite as Highly Efficient Photoelectrode for Hydrogen Production from Wastewater with Theoretical Study for the Water Splitting

Khalafalla

Hadia

Elsayed

et al. 2022

Adsorption Science & Technology

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“…The wavelength of the near-infrared (NIR) spectrum is between 780 nm and 2526 nm, and NIR can obtain molecular vibration information of hydrogen-containing groups, so it is widely used in the field of bioscience and related subjects [ 15 , 16 ]. NIR spectra have the characteristics of severe overlapping of spectral bands, so the spectra must be further analyzed with the help of knowledge and techniques in the field of chemometrics [ 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

Structural Analysis and Classification of Low-Molecular-Weight Hyaluronic Acid by Near-Infrared Spectroscopy: A Comparison between Traditional Machine Learning and Deep Learning

Tian

Zang

et al. 2023

Molecules

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Confusing low-molecular-weight hyaluronic acid (LMWHA) from acid degradation and enzymatic hydrolysis (named LMWHA–A and LMWHA–E, respectively) will lead to health hazards and commercial risks. The purpose of this work is to analyze the structural differences between LMWHA–A and LMWHA–E, and then achieve a fast and accurate classification based on near-infrared (NIR) spectroscopy and machine learning. First, we combined nuclear magnetic resonance (NMR), Fourier transform infrared (FTIR) spectroscopy, two-dimensional correlated NIR spectroscopy (2DCOS), and aquaphotomics to analyze the structural differences between LMWHA–A and LMWHA–E. Second, we compared the dimensionality reduction methods including principal component analysis (PCA), kernel PCA (KPCA), and t-distributed stochastic neighbor embedding (t-SNE). Finally, the differences in classification effect of traditional machine learning methods including partial least squares–discriminant analysis (PLS-DA), support vector classification (SVC), and random forest (RF) as well as deep learning methods including one-dimensional convolutional neural network (1D-CNN) and long short-term memory (LSTM) were compared. The results showed that genetic algorithm (GA)–SVC and RF were the best performers in traditional machine learning, but their highest accuracy in the test dataset was 90%, while the accuracy of 1D-CNN and LSTM models in the training dataset and test dataset classification was 100%. The results of this study show that compared with traditional machine learning, the deep learning models were better for the classification of LMWHA–A and LMWHA–E. Our research provides a new methodological reference for the rapid and accurate classification of biological macromolecules.

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“…This allows the robot to collect data and infer object material type before contact is made. Our research relies on the corpus of prior work in Near-Infrared (NIR) spectroscopy [3], [4], which has been demonstrated as a reliable means to estimate the material properties and composition of an object through reflected light in the ultraviolet (λ ≈ 300 nanometers) to the near-infrared (λ ≈ 1200 nanometers) spectra. Instead of representing light with three discrete values as is commonly done with red, blue, green (RBG) image channels, the integrated spectrometer represents light as a continuous curve, sampled at 2048 discrete values -allowing for the observation and quantification of changes in the spectral response of an object.…”

Section: Introductionmentioning

confidence: 99%

Pregrasp Object Material Classification by a Novel Gripper Design with Integrated Spectroscopy

Hanson¹,

Kelestemur²,

Erdoğmuş³

et al. 2022

Preprint

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Robots benefit from being able to classify objects they interact with or manipulate based on their material properties. This capability ensures fine manipulation of complex objects through proper grasp pose and force selection. Prior work has focused on haptic or visual processing to determine material type at grasp time. In this work, we introduce a novel parallel robot gripper design and a method for collecting spectral readings and visual images from within the gripper finger. We train a nonlinear Support Vector Machine (SVM) that can classify the material of the object about to be grasped through recursive estimation, with increasing confidence as the distance from the finger tips to the object decreases. In order to validate the hardware design and classification method, we collect samples from 16 real and fake fruit varieties (composed of polystyrene/plastic) resulting in a dataset containing spectral curves, scene images, and high-resolution texture images as the objects are grasped, lifted, and released. Our modeling method demonstrates an accuracy of 96.4% in classifying objects in a 32 class decision problem. This represents a performance improvement by 29.4% over the state of the art computer vision algorithms at distinguishing between visually similar materials. In contrast to prior work, our recursive estimation model accounts for increasing spectral signal strength and allows for decisions to be made as the gripper approaches an object. We conclude that spectroscopy is a promising sensing modality for enabling robots to not only classify grasped objects but also understand their underlying material composition.

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Advances in Near-Infrared Spectroscopy and Related Computational Methods

Abstract: Over the last few decades, near-infrared (NIR) spectroscopy has distinguished itself as one of the most rapidly advancing spectroscopic techniques [...]

Cited by 15 publications

References 35 publications

ATO/Polyaniline/PbS Nanocomposite as Highly Efficient Photoelectrode for Hydrogen Production from Wastewater with Theoretical Study for the Water Splitting

ATO/Polyaniline/PbS Nanocomposite as Highly Efficient Photoelectrode for Hydrogen Production from Wastewater with Theoretical Study for the Water Splitting

Structural Analysis and Classification of Low-Molecular-Weight Hyaluronic Acid by Near-Infrared Spectroscopy: A Comparison between Traditional Machine Learning and Deep Learning

Pregrasp Object Material Classification by a Novel Gripper Design with Integrated Spectroscopy

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