Terahertz Spectral Investigation of L-Cysteine Hydrochloride and its Monohydrate

An Interview with AI About Its Potential Role in Vibrational and Atomic Spectroscopy

Spectrophotometric Determination of Thiosulfate in Desulfurization Solutions 

Visible-NIR Spectral Characteristics Analysis Using Hyperspectral Data

Advancing Wetland Classification

We interviewed an AI program (ChatGPT) for 

 asking questions about AI and its role in various applications for vibrational and atomic spectroscopy, including data analysis. For vibrational spectroscopy we asked about Raman, Fourier transform infrared (FT-IR), near-infrared (NIR), ultraviolet-visible (UV-vis), terahertz (THz), nuclear magnetic resonance (NMR); for atomic spectroscopy we inquired about inductively coupled plasma-atomic emission spectroscopy (ICP-AES), inductively coupled plasma-mass spectrometry (ICP-MS), laser-induced breakdown spectroscopy (LIBS), X-ray fluorescence (XRF), and atomic absorption spectroscopy (AAS). We asked 20 general questions for this interview, keeping in mind that our readers want to know all they can about how to use AI for their analytical chemistry needs. We also included a set of references and further reading for those wishing to look more deeply into the subject of AI and spectroscopy.

We note that a formal definition of AI does not always indicate the use of neural networks. Neural networks are one of the many techniques used in AI, but they are not the only ones. AI encompasses a wide range of techniques and approaches, including rule-based systems, decision trees, genetic algorithms, and others (See our February, 2023 issue). While neural networks have gained prominence in recent years due to their ability to handle complex data and perform well on a variety of tasks, AI as a field is much broader and includes a range of techniques that do not rely on neural networks.

What are some of the specific ways in which AI is currently being used in vibrational and atomic spectroscopy?

AI is being used in spectroscopy in eight major ways. Refer to reference (1–15) for examples.

Spectral identification and interpretation: AI algorithms can help identify and interpret spectral data by comparing it to large spectral databases and providing an output that can be more easily understood by researchers.

Data preprocessing: AI can help preprocess spectral data by filtering out noise and other unwanted components that can affect the quality of the data.

Predictive modeling: AI can be used to develop predictive models that can accurately predict the properties of materials based on their spectral data.

Automation: AI can be used to automate the spectral analysis process, reducing the time and effort required by researchers to analyze large amounts of data.

Feature selection: AI can help select the most important features from spectral data, reducing the dimensionality of the data and making it easier to analyze.

Anomaly detection: AI can be used to detect anomalous spectral data, which can help identify unknown substances or detect changes in the properties of a material.

Quality control: AI can be used to monitor the quality of spectral data in real-time, allowing researchers to identify and correct issues as they arise.

Material discovery: AI can be used to screen large databases of spectral data to identify new materials with specific properties, accelerating the discovery of new materials for use in various applications.

How can AI be used for spectral interpretation in vibrational and atomic spectroscopy?

AI can be used for spectral interpretation in vibrational and atomic spectroscopy by training machine learning models to identify spectral features, classify compounds, and predict molecular properties. For example, neural networks can be trained on large spectral datasets to identify characteristic peaks in infrared and Raman spectra, allowing for automated compound identification and quantification. Support vector machines can also be used to classify unknown samples based on their spectral fingerprints. In addition, deep learning algorithms such as convolutional neural networks can be used to analyze atomic spectra to identify emission or absorption lines and predict atomic properties such as ionization energy and electron affinity. AI can also be used to optimize experimental parameters in spectroscopy, such as laser pulse duration and wavelength, to improve spectral resolution and sensitivity. Furthermore, AI can assist in the interpretation of complex spectra, such as those containing overlapping bands or multiple components, by using machine learning algorithms to deconvolute spectra and identify individual contributions. Finally, AI can aid in the design of new spectroscopic techniques and instruments, such as developing new laser sources or detectors optimized for specific spectral regions.

What are some of the challenges associated with using AI for spectral interpretation in vibrational and atomic spectroscopy?

While AI can improve spectral interpretation, there are challenges associated with its implementation in vibrational and atomic spectroscopy. Firstly, collecting large amounts of high-quality spectral data for AI training can be time-consuming and costly. Secondly, spectral variability can be challenging due to factors such as sample inhomogeneity and spectral noise. Thirdly, the complex and nonlinear relationships between the spectra and the underlying chemical information can make accurate spectral interpretation challenging. Fourthly, overfitting and underfitting can occur in the AI model, leading to overconfidence or underperformance. Fifthly, the black-box nature of some AI models can make it difficult to understand and interpret the reasoning behind the model's output. Lastly, the lack of standards for reporting AI models in spectroscopy can lead to a lack of transparency and reproducibility, hindering the wider adoption of AI in the field.

How can AI be used for quality control in vibrational and atomic spectroscopy?

AI can be used for quality control in vibrational and atomic spectroscopy in several ways. One such method is by developing models for the identification and quantification of impurities and contaminants in raw materials. AI algorithms can also be used to detect and classify samples that do not meet specified quality standards. Furthermore, AI can be used to monitor the consistency of production processes in real-time. This can be done by comparing real-time spectral data with a database of previously recorded spectral data from acceptable products. AI can also be used to identify trends in data that can be used to predict future quality control issues. Additionally, AI can be used to optimize the instrument settings and parameters for spectral acquisition to maximize spectral quality and resolution. Finally, AI can be used to automate routine spectral interpretation tasks to increase efficiency and reduce the potential for human error.

How can AI be used to optimize data acquisition in vibrational and atomic spectroscopy?

AI can be used to optimize data acquisition in vibrational and atomic spectroscopy by predicting the optimal experimental conditions to achieve the desired results. For example, AI algorithms can be used to predict the optimal laser intensity, wavelength, and pulse duration for Raman spectroscopy. Similarly, AI algorithms can be used to optimize the parameters of the excitation source for atomic spectroscopy. By using AI to optimize data acquisition, researchers can reduce experimental time and improve the accuracy and precision of their measurements. AI can also be used to reduce noise and artifacts in the acquired data. For example, AI algorithms can be used to remove baseline drifts, cosmic ray interference, and other sources of noise from Raman and atomic spectra. Additionally, AI can be used to reduce the amount of data required for accurate spectral analysis by identifying the most informative spectral regions and reducing the number of data points needed to obtain reliable results. Finally, AI can be used to automate the data acquisition process by controlling the instrument parameters in real-time based on the feedback from the analysis of the acquired spectra.

What are some of the key considerations when implementing AI for data analysis in vibrational and atomic spectroscopy?

When implementing AI for data analysis in vibrational and atomic spectroscopy, several key considerations should be taken into account. Firstly, the quality of the data being fed into the AI system is critical as poor quality data can significantly impact the accuracy and reliability of the results. Secondly, the AI algorithms used for data analysis must be chosen carefully and should be optimized for the specific application. Thirdly, the size and complexity of the data set must be considered, as large data sets can require significant computational resources and specialized hardware. Fourthly, the AI system must be regularly updated and trained on new data to ensure it remains accurate and up to date. Fifthly, the potential impact of false positives or false negatives should be considered, especially in applications where the results are critical for decision making. Finally, the ethical implications of using AI for data analysis should be considered, such as ensuring transparency, fairness, and accountability in the decision-making process.

How can AI be used for multivariate data analysis in vibrational and atomic spectroscopy?

AI can be used for multivariate data analysis in vibrational and atomic spectroscopy by extracting valuable information from complex datasets. One way to accomplish this is through the use of machine learning algorithms, such as principal component analysis (PCA) and partial least squares (PLS) regression. These algorithms can identify correlations between variables and create predictive models for spectral data. AI can also be used to perform feature selection, which involves identifying the most important variables for a given analysis. This reduces the dimensionality of the dataset and can improve model accuracy. Another approach is clustering, which groups similar samples based on their spectral properties. This can help identify trends or patterns in the data that may not be apparent otherwise. Additionally, AI can be used for anomaly detection, which involves identifying samples that deviate from expected patterns. Finally, deep learning techniques, such as convolutional neural networks (CNNs), can be used to analyze complex spectral images, such as those obtained through hyperspectral imaging. Overall, AI provides a powerful tool for multivariate data analysis in vibrational and atomic spectroscopy, enabling more accurate and efficient data interpretation.

How can AI improve the accuracy and precision of spectral analysis?

AI can improve the accuracy and precision of spectral analysis in several ways. Firstly, it can identify and correct errors in the data that can arise from factors such as noise, baseline drift, and spectral interferences. Secondly, it can identify and remove outliers, which can have a significant impact on the accuracy of the analysis. Thirdly, it can enable the automatic selection of the optimal method and parameters for spectral analysis, including the choice of preprocessing, peak detection, and calibration methods. Fourthly, it can enable the automated correction of spectral drift and other systematic errors that can affect the accuracy and precision of the analysis. Fifthly, it can enable the automated detection of spectral features that are difficult to identify by manual inspection, such as subtle changes in peak shape or position. Finally, it can enable the integration of data from multiple sources, such as different instruments or data types, to improve the accuracy and precision of the analysis.

What are some challenges in applying AI to atomic spectroscopy?

Applying AI to atomic spectroscopy has some challenges that need to be addressed. One of the primary challenges is the limited availability of labeled data, as the cost of labeling atomic spectra can be expensive. Additionally, the noise and variability in atomic spectra data can make it difficult for AI algorithms to distinguish between different signals, leading to errors in classification and prediction. Another challenge is the complexity of atomic spectra, which can make it difficult to extract meaningful features for analysis. This complexity is exacerbated by the fact that different elements can produce very similar spectra, making it difficult for AI algorithms to distinguish between them. The diversity of instruments and experimental conditions used to acquire atomic spectra can also pose challenges, as these factors can introduce additional variability in the data. Finally, there is a need for standardization of data acquisition and processing protocols to ensure that the AI algorithms can be trained on consistent data, which can be challenging given the diversity of techniques used in atomic spectroscopy.

Can AI be used to identify unknown compounds in spectral data?

Yes, AI can be used to identify unknown compounds in spectral data. One approach is to use machine learning algorithms that are trained on a large database of known compounds. The algorithm can then be used to analyze the spectral data of an unknown compound and compare it to the database to identify the closest match. Another approach is to use deep learning techniques such as convolutional neural networks (CNNs) to analyze the spectral data directly. The CNN can learn to recognize patterns in the spectral data that are characteristic of specific compounds and use this information to identify unknown compounds.

However, there are challenges in using AI for compound identification, including the need for high-quality spectral data, the availability of a comprehensive database of known compounds, and the potential for false positive and false negative results. The accuracy of AI-based identification also depends on the similarity of the unknown compound to the compounds in the database and the quality of the spectral data. Another challenge is the interpretation of the results, as the output of the AI algorithm may not be easily interpretable by non-experts, requiring additional analysis and validation. Therefore, the use of AI for compound identification should be complemented by expert knowledge and manual validation to ensure the accuracy and reliability of the results.

Can AI be used to improve the accuracy and sensitivity of Raman spectroscopy?

Yes, AI has been applied to improve the performance of Raman spectroscopy. For example, machine learning algorithms have been used to reduce the effects of sample variability and to improve the signal-to-noise ratio in Raman spectra. Additionally, AI can be used to develop models that can predict the properties of samples based on their Raman spectra, enabling more accurate and sensitive analysis (4–6).

How can AI improve the speed and efficiency of data analysis in atomic spectroscopy?

AI can improve the speed and efficiency of data analysis in atomic spectroscopy by automating data processing and analysis. For example, machine learning algorithms can be used to automatically detect and quantify trace elements in complex samples, reducing the time and effort required for manual analysis. Additionally, AI can be used to optimize experimental parameters and improve the accuracy and precision of atomic spectroscopy measurements.

Can AI be used to identify and classify unknown compounds in NMR spectra?

Yes, AI can be used to identify and classify unknown compounds in NMR spectra. For example, machine learning algorithms can be used to create models that can accurately predict the chemical shifts of various atoms in a molecule, enabling identification of unknown compounds based on their NMR spectra. Additionally, AI can be used to classify compounds based on their NMR spectra, enabling rapid identification of similar compounds and aiding in the discovery of new molecules (7).

How can AI be used to optimize experimental parameters in ICP-MS analysis?

AI can be used to optimize experimental parameters in ICP-MS analysis by enabling automated and intelligent parameter selection. For example, machine learning algorithms can be used to predict the optimal parameters for ICP-MS analysis based on the characteristics of the sample and the desired analytical performance. Additionally, AI can be used to monitor and adjust experimental parameters in real-time, enabling more efficient and effective analysis (8–9).

Can AI be used to improve the accuracy and precision of XRF analysis?

Yes, AI can be used to improve the accuracy and precision of XRF analysis. For example, machine learning algorithms can be used to correct for matrix effects and sample variability, enabling more accurate and precise quantification of trace elements in complex samples. Additionally, AI can be used to optimize experimental parameters and improve the sensitivity of XRF analysis, enabling detection of trace elements at lower concentrations.

How can AI be used to predict the properties of materials based on their UV-vis spectra?

AI can be used to predict the properties of materials based on their UV-vis spectra by creating predictive models using machine learning algorithms. For example, models can be developed to predict the bandgap energy, refractive index, and other optical properties of materials based on their UV-vis spectra. Additionally, AI can be used to classify materials based on their UV-vis spectra, enabling rapid identification of materials with similar properties (10).

Can AI be used to automate the identification of peaks in FT-IR spectra?

Yes, AI can be used to automate the identification of peaks in FT-IR spectra. For example, machine learning algorithms can be used to detect and classify peaks in FT-IR spectra, enabling automated and accurate identification of spectral features. Additionally, AI can be used to remove noise and reduce the effects of sample variability, improving the accuracy and sensitivity of FT-IR spectroscopy (11–13).

How can AI be used to optimize experimental parameters in spectroscopy?

AI can be used to optimize experimental parameters in spectroscopy by leveraging machine learning techniques to identify the most important features in the data and determine the optimal conditions for data collection. For example, AI models can be used to identify the optimal wavelength range or spectral resolution for a particular analysis, or to optimize sample preparation or instrument settings. This can lead to more efficient and accurate spectroscopic analyses.

What role can AI play in the development of new spectroscopic techniques?

AI can play a key role in the development of new spectroscopic techniques by providing insights into the underlying physical processes that govern the spectral data. For example, machine learning models can be used to identify correlations between different spectral features and physical properties of the sample, leading to new insights into the molecular structure or chemical composition of the material. Additionally, AI can be used to design new experimental setups or optimize existing methods to improve the accuracy and sensitivity of spectroscopic measurements.

What are some potential future developments in AI-assisted spectroscopy?

One potential future development in AI-assisted spectroscopy is the integration of multiple spectroscopic techniques to provide a more comprehensive analysis of a sample. AI models can be used to combine data from different sources and identify correlations between different spectral features, leading to a more complete understanding of the sample composition and properties. Additionally, there is potential for AI to improve the speed and efficiency of spectroscopic analyses, allowing for real-time monitoring of chemical reactions or other dynamic processes. As AI technology continues to evolve, there are many exciting possibilities for the future of spectroscopic analysis (13–15).

(1) Xu, Y.; Liu, X.; Cao, X.; Huang, C.; Liu, E.; Qian, S.; Liu, X.; Wu, Y.; Dong, F.; Qiu, C. W.; Qiu, J. Artificial Intelligence: A Powerful Paradigm for Scientific Research. 

https://doi.org/10.1016/j.xinn.2021.100179

(2) Cova, T. F.; Pais, A. A. Deep Learning for Deep Chemistry: Optimizing the Prediction of Chemical Patterns. 

(3) Yang, J.; Xu, J, Zhang, X, Wu, C, Lin, T, Ying, Y. Deep Learning for Vibrational Spectral Analysis: Recent Progress and a Practical Guide. 

https://doi.org/10.1016/j.aca.2019.06.012

(4) Lussier, F.; Thibault, V.; Charron, B.; Wallace, G. Q.; Masson, J. F. Deep Learning and Artificial Intelligence Methods for Raman and Surface-Enhanced Raman Scattering. 

https://doi.org/10.1016/j.trac.2019.115796

(5) Luo, R., Popp, J.; Bocklitz, T. Deep Learning for Raman Spectroscopy: A Review. 

(6) Ho, C. S.; Jean, N.; Hogan, C. A.; Blackmon L.; Jeffrey, S. S.; Holodniy, M.; Banaei, N.; Saleh, A. A.; Ermon, S.; Dionne, J. Rapid Identification of Pathogenic Bacteria Using Raman Spectroscopy and Deep Learning. 

https://doi.org/10.1038/s41467-019-12898-9

(7) Chen, D.; Wang, Z.; Guo, D.; Orekhov, V.; Qu, X. Review and Prospect: Deep Learning in Nuclear Magnetic Resonance Spectroscopy. 

(8) Sun, G.; Zeng, Q.; Zhou, J. X. Machine Learning Coupled with Mineral Geochemistry Reveals the Origin of Ore Deposits. 

https://doi.org/10.1016/j.oregeorev.2022.104753

(9) Flores, K.; Turley, R. S.; Valdes, C.; Ye, Y.; Cantu, J.; Hernandez-Viezcas, J. A.; Parsons J. G.; Gardea-Torresdey, J. L. Environmental Applications and Recent Innovations in Single Particle Inductively Coupled Plasma Mass Spectrometry (SP-ICP-MS). 

https://doi.org/10.1080/05704928.2019.1694937

(10) Guo, Y.; Liu C.; Ye, R.; Duan, Q. Advances on Water Quality Detection by UV-vis Spectroscopy. 

(11) Yue, F.; Chen, C.; Yan, Z.; Chen, C.; Guo, Z.; Zhang, Z.; Chen, Z.; Zhang, F.; Lv, X. Fourier Transform Infrared Spectroscopy Combined with Deep Learning and Data Enhancement for Quick Diagnosis of Abnormal Thyroid Function. 

https://doi.org/10.1016/j.pdpdt.2020.101923

(12) Calle, J. L.; Ferreiro-González, M.; Ruiz-Rodríguez, A.; Fernández, D.; Palma, M. Detection of Adulterations in Fruit Juices Using Machine Learning Methods Over FT-IR Spectroscopic Data. Agronomy. 

(13) Xu, J. L.; Riccioli, C.; Herrero-Langreo, A.; Gowen A, A. Deep Learning Classifiers for Near Infrared Spectral Imaging: A Tutorial. 

(14) Zhang, X.; Yang, J.; Lin, T.; Ying, Y. Food and Agro-Product Quality Evaluation Based on Spectroscopy and Deep Learning: A Review. 

https://doi.org/10.1016/j.tifs.2021.04.008

(15) Gunaratne, T. M.; Gonzalez, Viejo C.; Gunaratne, N. M.; Torrico, D. D.; Dunshea, F. R.; Fuentes, S. Chocolate Quality Assessment Based on Chemical Fingerprinting Using Near Infra-red and Machine Learning Modeling. 

 serves on the Editorial Advisory Board of Spectroscopy and is the Senior Technical Editor for 

. He is also a Certified Core Adjunct Professor at U.S. National University in La Jolla, California. He was formerly the Executive Vice President of Research and Engineering for Unity Scientific and Process Sensors Corporation.

Articles

We interviewed an AI program (ChatGPT) for Spectroscopy asking questions about AI and its role in various applications for vibrational and atomic spectroscopy, including data analysis. 

Spectrochimica Acta Part B: Atomic Spectroscopy

, Alexandre Kolomenskii and coworkers from Texas A&M University in College Station, Texas, delves into the hyperfine structure (HFS) and isotope shifts (ISs) of xenon using Doppler-free saturated absorption spectroscopy. The research focuses on near-infrared (NIR) transitions in the 820–841 nm spectral range, utilizing commercial xenon gas samples with natural isotopic abundances.

Modern car xenon lamp headlight | Image Credit: © Scanrail - stock.adobe.com

Doppler-free saturated absorption spectroscopy

 is a technique used to achieve high-resolution spectroscopic measurements by eliminating the broadening effects caused by the Doppler effect. It involves employing two laser beams to interact with the atoms or molecules of interest. One laser beam excites the atoms to an excited state, while the other beam probes the absorption of the excited atoms. By carefully adjusting the frequencies of the two laser beams, the Doppler broadening caused by thermal motion of the atoms can be canceled out, allowing for precise observation of fine spectral features such as hyperfine structure and isotope shifts. This technique is particularly useful when studying transitions with small frequency variations, enabling detailed investigations of atomic or molecular properties with exceptional accuracy. Doppler-free saturated absorption spectroscopy has applications in various fields, including atomic physics, spectroscopy, precision measurements, and laser frequency stabilization.

To facilitate their investigation, the researchers employed a highly tunable narrow-line Ti:sapphire laser for precise measurements. Notably, the team successfully resolved the hyperfine structures of 

Xe, isotopes that possess nonzero magnetic moments and electric quadrupoles, leading to significant HFS line splitting. Additionally, the study observed clear isotope shifts for even isotopes near wavelengths of 820.860 nm and 841.150 nm in vacuum.

A Ti:sapphire laser is a solid-state laser that uses a titanium-doped sapphire crystal as its gain medium. It emits intense, ultrashort pulses of laser light in the NIR to visible wavelength range. Ti:sapphire lasers are widely used in scientific research for applications such as ultrafast spectroscopy and microscopy, thanks to their tunability, high power, and femtosecond pulse duration.

Through comprehensive modeling of the observed absorption lines, the researchers achieved qualitative replication of the experimental spectral profiles. The absorption peaks corresponding to the hyperfine structure and isotope shifts of xenon isotopes serve as distinctive spectral fingerprints for their identification. The ultimate objective lies in the quantitative spectral analysis, enabling the determination of isotopic abundances in natural xenon gas samples, including both stable and radioactive isotopes.

The investigation of hyperfine structure and isotope shifts in atomic spectra offers valuable insights into nuclear spins, moments, and charge distribution. xenon, with its seven stable isotopes and various natural abundances, provides an intriguing subject for such studies. The odd isotopes 

Xe, in particular, exhibit HFS because of their nonzero nuclear spin, while 

Xe, characterized by closed shells and 82 neutrons, behaves as a magic nucleus.

The ability to accurately measure hyperfine structure and isotope shifts is vital for numerous applications. In the case of xenon gas, it holds potential for highly sensitive detection of underground or underwater nuclear explosions, astrophysical research including meteorite dating, atmospheric studies, and medical applications.

This work opens avenues for further investigations into the isotopic abundances of xenon gas, contributing to advancements in diverse fields ranging from nuclear security to atmospheric and astrophysical research. The findings of this study serve as a testament to the power of Doppler-free saturated absorption spectroscopy in unraveling the intricate characteristics of Xe's hyperfine structure and isotope shifts.

(1) Bounds, J.; Kolomenskii, A.; Trainham, R.; Manard, M.; Schuessler, H.Hyperfine structure and isotope shifts of xenon measured for near-infrared transitions with Doppler-free saturated absorption spectroscopy. 

Spectrochimica Acta Part B: At. Spectrosc

Researchers explore xenon's hyperfine structure and isotope shifts using Doppler-free saturated absorption spectroscopy.

Hyperfine Structure and Isotope Shifts of Xenon Explored with Doppler-Free Saturated Absorption Spectroscopy

, researchers from Ariel University in Israel, led by L. Nagli, M. Gaft, and Y. Raichlin, have introduced innovative laser-induced breakdown spectroscopy–molecular LIBS–molecular laser-induced fluorescence (LIBS-MLIBS-MLIF) methods for the determination of beryllium concentrations (1). The study presents a solution to the challenges posed by self-absorption effects and the interference of other species in laser-induced plasma (LIP) during beryllium analysis.

99.58% fine beryllium isolated on white background | Image Credit: © Björn Wylezich - stock.adobe.com

LIBS-MLIBS-MLIF is a combined technique of LIBS, which focuses a laser onto a sample, generating a plasma that emits characteristic wavelengths of light for element identification and quantification; MLIBS that expands on LIBS by analyzing molecular emissions, particularly diatomic molecules, to provide additional compositional insights; and MLIF, which employs lasers to excite specific molecules, measuring their emitted fluorescence for identification and quantification. By combining LIBS, MLIBS, and MLIF methods, researchers can enhance the accuracy and sensitivity of beryllium determination, overcoming challenges like self-absorption effects and interference from other species.

Beryllium, a strategic and critical material, possesses exceptional physical properties, including high strength-to-weight ratio, high melting point, thermal stability and conductivity, reflectivity, and X-ray transparency. These characteristics make it indispensable in various industries such as aerospace, telecommunications, defense, medical, and nuclear sectors. However, exposure to beryllium particles or contact with beryllium materials can lead to severe lung-related diseases, including acute beryllium disease and lung cancer. Therefore, accurate and efficient analysis of beryllium content is of utmost importance.

The researchers propose the non-resonant beryllium I line at 457.27 nm as a viable option for quantitative Be analysis using the LIBS technique. This alternative wavelength range circumvents the self-absorption effect encountered with the resonant beryllium II doublet at 313.04–313.11 nm, which is typically used for trace beryllium concentrations. By expanding the concentration range from 0.1 to 5 wt% in samples such as Cu-Be bronze, the proposed method overcomes the limitations of resonant lines.

Furthermore, the study explores the use of MLIF of BeO diatomic molecules as a complementary approach for complex cases. The MLIF method proves highly suitable in scenarios where both atomic and molecular LIBS methods suffer from overlapping emissions with other species, as observed in Al-Be bronze.

The significance of these advancements lies in their potential applications across various industries and sectors, including medicine and environmental control, where precise measurement of Be concentrations down to tenths of ppm is necessary. The LIBS-MLIBS-MLIF methods presented in this study offer promising prospects for accurate and reliable Be analysis, enabling efficient quality control, occupational safety, and environmental monitoring.

The researchers anticipate that their findings will drive further exploration of LIBS-MLIBS-MLIF techniques in beryllium determination, facilitating improved understanding, control, and mitigation of health risks associated with beryllium exposure. The study's outcomes contribute to ongoing efforts aimed at enhancing workplace safety standards and protecting individuals working with beryllium-containing materials.

As the demand for beryllium continues to grow across industries, the implementation of advanced analytical methods holds the key to ensuring the safe and responsible utilization of this critical material. This research marks a significant milestone in the field of beryllium analysis, opening new avenues for accurate and comprehensive assessment of beryllium content.

(1) Nagli, L.; Gaft, M.; Raichlin, Y.LIBS-MLIBS-MLIF methods: Beryllium determination. 

Spectrochimica Acta Part B: At. Spectrosc. 

New combined analytical techniques unveiled for accurate beryllium determination using LIBS-MLIBS-MLIF methods show promise.

New Advancements in Beryllium Analysis: LIBS-MLIBS-MLIF Methods for Beryllium Determination 

Mercury contamination poses a significant risk to human health because of its persistence in the environment and the potential for bioaccumulation through the food chain. To address this challenge, scientists from the People’s Hospital of Zhengzhou University and Luoyang Normal University have developed an innovative fluorescent probe for the selective and real-time detection of mercury ions (Hg

Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy

, presents a breakthrough in mercury detection techniques (1).

Small mercury (Hg) metal sign with drops | Image Credit: © alexus - stock.adobe.com

The team designed and synthesized a novel fluorescent probe called (E)-O-(4-(2-(3-(dicyanomethylene)-5,5-dimethylcyclohex-1-en-1-yl)vinyl)phenyl) O-phenyl carbonothioate (ICM-Hg). This probe demonstrated remarkable selectivity and sensitivity in detecting Hg

 in various samples, including water, food, and live cells.

ICM-Hg operates through an intramolecular charge transfer (ICT) process, which is the basis of its mercury detection mechanism. When ICM-Hg comes into contact with mercury ions (Hg

), it triggers an ICT process within the probe molecule. This process leads to a distinct color change from colorless to bright yellow and a significant increase in yellow fluorescence emission, allowing for the selective and sensitive detection of mercury ions. With

 triggers this distinct color change from colorless to bright yellow. The fluorescence emission at 585 nm exhibited a linear relationship within the concentration range of Hg

 (0–45 μM). The detection limit for Hg

 was calculated to be 231 nM, highlighting the probe's high sensitivity.

One of the significant advantages of ICM-Hg is its ability to detect Hg

 in real samples, such as tap water, tea, shrimp, and crab. The probe demonstrated excellent quantitative recovery, ensuring accurate analysis of Hg

 contamination in these commonly encountered samples.

In addition to environmental and food sample analysis, the researchers successfully applied ICM-Hg for intracellular fluorescence imaging. This capability allows for the visualization and tracking of Hg

 within live cells, opening new avenues for studying the distribution and behavior of mercury ions in biological systems.

The development of this innovative fluorescent probe provides a rapid and reliable method for detecting and monitoring mercury contamination in water, food, and living organisms. The high selectivity, sensitivity, and real-time detection capabilities of ICM-Hg hold immense promise for environmental monitoring, food safety, and biomedical applications.

(1) Li, X.; Chu, D.; Wang, J.; Qi, Y.; Yuan, W.; Li, J. Zhou, Z.A dicyanoisophorone-based ICT fluorescent probe for the detection of Hg

 in water/food sample analysis and live cell imaging. 

Spectrochimica Acta Part A: Mol. Biomol. Spectrosc. 

Scientists create a highly selective fluorescent probe, ICM-Hg, for real-time detection of mercury ions (Hg2+) in water, food samples, and live cells.

Innovative Fluorescent Probe Enables Rapid Detection of Mercury in Water, Food Samples, and Live Cells

Deoxyribonucleic acid (DNA) biosensors have been integral to revolutionizing gene mutation detection and pathogen identification. Among the various DNA detection techniques available, surface-enhanced Raman scattering (SERS) has emerged as a promising approach. In a recent study published in the 

, Andrzej Kudelski from the University of Warsaw and his team shed light on the critical factors influencing SERS DNA sensors (1).

Abstract blue colored shiny dna molecule on futuristic digital illustration background. | Image Credit: © robsonphoto - stock.adobe.com

The research focused on the impact of the plasmonic metal employed in the SERS substrate and the design of sandwich-type biosensors. The sandwich-type construction refers to the arrangement of the Raman reporter in close proximity to the DNA structure.

By systematically investigating the influence of distance factors on the SERS signal enhancement, the researchers uncovered several important findings. To start off, the study revealed that increasing the proximity between the Raman scatterer and the SERS-active surface did not always result in higher signal intensity, which is a bit of evidence that cuts against conventional wisdom. In fact, the measured Raman signal was more intense when the Raman reporter was placed further away from the plasmonic substrate.

SERS DNA sensors operate through the interaction of light with plasmonic metal nanostructures, such as gold or silver nanoparticles. These nanostructures generate intense electric fields called "hotspots" near the metal surface because of localized surface plasmons. DNA molecules immobilized on the metal substrate interact with these hotspots, amplifying the Raman scattering signal. Raman scattering occurs when incident light interacts with molecules, causing them to emit scattered light with unique spectral fingerprints. SERS DNA sensors utilize plasmonic metal nanostructures to enhance the Raman scattering signal, enabling sensitive detection and characterization of specific DNA sequences. This makes SERS DNA sensors valuable in genetic analysis, diagnostics, and molecular biology research.

The observed phenomenon is believed to be attributed to the diverse structures of the DNA sensors and the resulting variation in hybridization efficiency. It is hypothesized that steric hindrances play a significant role in modulating the hybridization process.

The team conducted a comprehensive analysis of different configurations and measured the corresponding SERS signal intensities. The results indicated that optimizing the positioning of the Raman reporter and mismatches within the sandwich-type DNA sensor is crucial for achieving optimal signal enhancement.

These findings contribute to a deeper understanding of the underlying mechanisms in SERS DNA sensors. They highlight the importance of carefully designing the construction strategy and considering the influence of the plasmonic metal employed in the SERS substrate.

Further exploration of these factors could potentially enhance the sensitivity and accuracy of SERS DNA sensors. The insights gained from this study may pave the way for improved DNA detection techniques, facilitating advancements in genetic research, diagnostics, and pathogen detection.

In summary, Andrzej Kudelski and his team at the University of Warsaw have elucidated the influence of the sandwich-type DNA construction strategy and plasmonic metal on the signal generated by SERS DNA sensors. Their findings underscore the importance of optimizing these factors to maximize the performance of SERS-based DNA detection platforms.

(1) Pyrak, E.; Kowalczyk, A.; Weyher, J. L.; Nowicka, A. M.; Kudelski, A.Influence of sandwich-type DNA construction strategy and plasmonic metal on signal. 

Researchers explore the impact of sandwich-type DNA construction and plasmonic metal on the signal generated by surface-enhanced Raman scattering (SERS) DNA sensors, giving insight on optimization strategies for improved detection.

Optimizing SERS DNA Sensors: Impact of Sandwich-Type Construction and Plasmonic Metal Revealed

In an effort to combat the deceptive practice of selling aged Ningxia wolfberry as fresh produce, researchers at Hunan University in Changsha, PR China, have developed a novel method for rapidly identifying the storage year of this valuable fruit. Their study, published in 

, introduces the combination of front-face excitation-emission matrix (FF-EEM) fluorescence spectroscopy and interpretable deep learning as a powerful tool for this purpose (1).

 is a technique that involves illuminating a sample with various wavelengths of light and measuring the corresponding emitted fluorescence. It provides a comprehensive spectral fingerprint of the sample, capturing its unique fluorescence characteristics. By analyzing the excitation and emission spectra using deep learning, valuable chemical information about the sample can be extracted. Deep learning is a branch of machine learning that uses artificial neural networks to learn and make predictions from complex data.

Ningxia wolfberry, known for its antioxidant properties and health benefits, fetches premium prices in the market. However, unscrupulous traders may attempt to pass off old wolfberries as freshly harvested ones, leading to potential consumer deception and financial losses. The proposed method aims to address this challenge by providing a lossless, fast, and accurate means of determining the storage year of Ningxia wolfberry.

The researchers employed the alternating trilinear decomposition (ATLD) algorithm to extract chemically significant information from the three-way data array obtained from Ningxia wolfberry samples. This preprocessing step allowed for the isolation of key features related to the storage years of the fruit. Furthermore, the team introduced a convolutional neural network (CNN) model called EEMnet, specifically designed for the identification of storage years. EEMnet leverages the subtle spectral differences among wolfberry samples to achieve robust classification.

Remarkably, the EEMnet model exhibited a correct classification rate exceeding 98% for the training set, test set, and prediction set, showcasing its effectiveness in distinguishing wolfberry samples of different storage years. To enhance transparency and interpretability, the researchers conducted a series of analyses to unravel the inner workings of the deep learning model, offering insights into the decision-making process.

The results of this study demonstrate that the combined approach of FF-EEM fluorescence spectroscopy and EEMnet holds great potential for rapid and accurate identification of the storage year of Ningxia wolfberry. By providing a green and reliable solution, this method offers a valuable contribution to the identification of storage years for Chinese medicinal materials.

As the demand for high-quality agricultural products continues to rise, the development of innovative techniques like FF-EEM fluorescence spectroscopy coupled with interpretable deep learning paves the way for improved food authentication, ensuring consumer confidence and safeguarding the integrity of the market. The findings from this research could potentially be applied to other valuable food products, enabling reliable traceability and quality control across various industries.

(1) Yan, X.-Q.; Wu, H.-L.; Wang, B.; Wang, T.; Chen, Y.; Chen, A.-Q.; Huang, K.; Chang, Y.-Y.; Yang, J.; Yu, R.-Q. Front-face excitation-emission matrix fluorescence spectroscopy combined with interpretable deep learning for the rapid identification of the storage year of Ningxia wolfberry. 

Spectrochimica Acta Part A: Mol. Biomol. Spectrosc.

Researchers have developed a rapid and accurate method combining front-face excitation-emission matrix fluorescence spectroscopy and interpretable deep learning to identify the storage year of Ningxia wolfberry, offering a green solution to combat fraudulent practices in the market.

Front-face Excitation-Emission Matrix Fluorescence Spectroscopy and Interpretable Deep Learning Unveil Storage Years of Ningxia Wolfberry

Researchers from Dalian Polytechnic University in China have designed and synthesized a new fluorescent probe that can selectively detect aluminum ions (Al

). The probe, called (E)-((2,4-dihydroxybenzyl)diazenyl)(pyridin-2-yl)methanone (HL), was studied using density functional theory (DFT) and time-dependent density functional theory (TD-DFT) to understand its excited state dynamics mechanism.

Atom icon isolated on transparent background. Fusion orbit spin. Neon light atomic neutron. Atom blue color. Nuclear atom. 3d cell nucleus. Molecule fusion. Proton core symbol. Ion element. Vector | Image Credit: © Omeris - stock.adobe.com

, provides a better understanding of the excited state dynamics mechanism of HL and may lead to the development of more effective fluorescent probes for detecting metal ions (1).

DFT and TD-DFT are widely used computational tools to understand the excited state dynamics mechanisms of molecules. DFT is used to calculate the potential energy surfaces (PESs) of molecules and determine the probability of various excited state pathways. TD-DFT is used to calculate the electronic transitions and predict the spectral properties of molecules. These techniques can provide valuable insights into the electronic structure and excited state dynamics of molecules, which are crucial for designing new materials with desired optical properties. In this specific study, DFT and TD-DFT were used to explain the fluorescence quenching and enhancement behaviors of a fluorescent probe designed to detect Al

The researchers found that the excited-state intramolecular proton transfer (ESIPT) process hardly occurs because of high reaction barriers, which means that the fluorescence quenching behavior of HL is not based on ESIPT. Instead, an electron on C=N in HL can be transferred to the fluorophore during excitation, accompanied by the photo-induced electron transfer (PET) process. The excited state can then undergo a twisted intramolecular charge transfer (TICT) process, which releases non-radiative decay.

The ESIPT process is a mechanism where the excited-state proton is transferred from a donor group to an acceptor group within the same molecule. In contrast, the photo-induced electron transfer (PET) process involves the transfer of an electron from a donor to an acceptor molecule upon photoexcitation. The twisted intramolecular charge transfer (TICT) process refers to the excited-state phenomenon in which a molecule undergoes a geometrical distortion that facilitates the transfer of charge from one end to another through a twisted intramolecular pathway. In the case of HL, the PET process occurred in the absence of Al

, accompanied by an electron transfer from C=N to the fluorophore. The excited state of HL could undergo a TICT process, releasing non-radiative decay. After binding to Al

, the PET process no longer occurred, and the TICT process was eliminated, resulting in a significant fluorescence enhancement.

, the PET process no longer occurs, and the TICT process is eliminated, resulting in a significant enhancement in fluorescence. The researchers' calculation results explain the quenching and enhancement behaviors of fluorescence before and after the reaction with Al

The new probe is not only effective but also selective in detecting Al

. This is important as aluminum is widely used in industries such as construction, transportation, and packaging, and overexposure to aluminum can have negative effects on human health.

(1) Li, W.; Liu, X.; Wang, Y.; Wang, Y.; Hou, Y.; Tian, J.; Fei, X. Investigation on Non-radioactive Behavior of an Acylhydrazone-based Fluorescent Probe: Coexistence of PET and TICT Mechanisms. 

A new fluorescent probe designed and synthesized by researchers can effectively and selectively detect Al3+ ions through the coexistence of photo-induced electron transfer (PET) and twisted intramolecular charge transfer (TICT) mechanisms.

New Fluorescent Probe Developed for Detecting Aluminum (Al3+) Ions

Researchers at Universidade Federal do Maranhão in São Luís, Brazil, have conducted an 

 high-temperature Raman scattering study of monoclinic silver dimolybdate (m-Ag

) microrods. The study, published in the 

 journal, explored the temperature-dependent behavior of the m-Ag

 microrods and found an irreversible first-order structural phase transition at 698 K–723 K and a melting process at 773 K (1).

 microrods by using the conventional hydrothermal method at 423 K for 24 h. Then, they conducted structural and morphological characterization of the sample using powder X-ray diffraction (XRD) and scanning electron microscopy (SEM), respectively. The Raman scattering measurements were performed on m-Ag

 microrods to determine the phase transition, and doing so revealed changes in the Raman spectra, confirming the transition from the P21/c monoclinic structure to the P-1 triclinic structure.

) microrods have potential applications in various fields, such as catalysis, electrochemistry, and gas sensing. The study of their structural and morphological properties, as well as their temperature-dependent behavior, is important for understanding their potential applications and for developing new ones. The 

 high-temperature Raman scattering study of m-Ag

 microrods presented in the article provides valuable insights into their phase transition behavior and melting process, which can inform future research on this subject.

Interestingly, no morphological changes were observed during the structural phase transition of the sample at 723 K. However, time-dependent optical microscopy at 773 K showed the growth of nanowires on the Ag

 microrods in the triclinic structure. This study provides significant insights into the temperature-dependent behavior of m-Ag

 microrods and their potential use in high-temperature applications.

This paper, with lead author J.V.B. Moura, indicated that Raman spectra provide important insights into the high-temperature phase transition behavior of m-Ag

 microrods, which is essential for their practical applications in various fields. This study could lead to further research on the phase transition behavior of other materials and could potentially open up new avenues for their use in high-temperature applications.

Overall, this study highlights the importance of understanding the temperature-dependent behavior of materials and their potential for practical applications. The findings of this study could have significant implications in various fields, such as materials science, chemistry, and physics.

(1) Ferreira, A. N. C.; Ferreira, W. C.; Duarte, A. V.; Santos, C. C.; Freire, P. T. C.; Luz-Lima, C.; Moura, J. V. B. In situ high‐temperature Raman scattering study of monoclinic Ag

An in situ high-temperature Raman scattering study of monoclinic m-Ag2Mo2O7 microrods reveals an irreversible first-order structural phase transition and melting process, according to new research.

Researchers Use Raman Spectroscopy to Study Phase Transition of Monoclinic Silver Dimolybdate Microrods at High Temperatures

Scientists at Texas Christian University in Fort Worth have discovered a new form of phosphorescence in 2-aminopyridine (2APi) using direct triplet state excitation. The team was able to observe room temperature phosphorescence in 2APi embedded in poly (vinyl alcohol) (PVA) films. The findings were published in

 Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy

A recent study has reported the observation of room temperature phosphorescence in 2-aminopyridine (2APi) embedded in poly (vinyl alcohol) (PVA) films with direct triplet state excitation, which could have implications for future technological applications.

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