Analytical Applications Research Articles

Performance Comparison of Ambient Ionization Techniques Using a Single Quadrupole Mass Spectrometer for the Analysis of Amino Acids, Drugs, and Explosives.

The utilization of ambient ionization (AI) techniques for mass spectrometry (MS) has significantly grown due to their ability to facilitate rapid and direct sample analysis with minimal sample preparation. This study investigates the performance of various AI techniques, including atmospheric solids analysis probe (ASAP), thermal desorption corona discharge (TDCD), direct analysis in real time (DART), and paper spray coupled to a Waters QDa mass spectrometer. The focus is on evaluating the linearity, repeatability, and limit of detection (LOD) of these techniques across a range of analytes, including amino acids, drugs, and explosives. The results show that each AI technique exhibits distinct advantages and limitations. ASAP and DART cover high concentration ranges, which may make them suitable for semiquantitative analysis. TDCD demonstrates exceptional linearity and repeatability for most analytes, while paper spray offers surprising LODs despite its complex setup (between 80 and 400 pg for most analytes). The comparison with electrospray ionization (ESI) as a standard method shows that ambient ionization techniques can achieve competitive LODs for various compounds such as PETN (80 pg ESI vs 100 pg ASAP), TNT (9 pg ESI vs 4 pg ASAP), and RDX (4 pg ESI vs 10 pg ASAP). This study underscores the importance of selecting the appropriate ambient ionization technique based on the specific analytical requirements. This comprehensive evaluation contributes valuable insights into the selection and optimization of AI techniques for diverse analytical applications.

Performance aware algorithm design for elastic resource workflow management of cluster consolidation to handle enterprise big data

<span lang="EN-US">Integration and deployment of big data and business analytics application with cloud computing are more attractive as a service and are trending practice. This hybrid workflow is rapidly increasing and will trigger a revolution for enterprise data handling, information retrieval and computing. This paper presents hybrid workflow management framework for big data and multi cloud computing systems in a two-step approach. Linear optimization-based resource assessment algorithm is planned in the first step. Cluster oriented elastic resource allocation and workflow management techniques are concentrated in the second step. This paper also focus on performance evaluation parameters includes execution time, through put with multi task work flow optimization model. The proposed framework is efficiently managed the implementation of hybrid workflows by finetuning the evaluation attributes and provides improvement in terms of response time an average of 6%.</span>

A Survey on Progressive Visualization.

Currently, growing data sources and long-running algorithms impede user attention and interaction with visual analytics applications. Progressive visualization (PV) and visual analytics (PVA) alleviate this problem by allowing immediate feedback and interaction with large datasets and complex computations, avoiding waiting for complete results by using partial results improving with time. Yet, creating a progressive visualization requires more effort than a regular visualization but also opens up new possibilities, such as steering the computations towards more relevant parts of the data, thus saving computational resources. However, there is currently no comprehensive overview of the design space for progressive visualization systems. We surveyed the related work of PV and derived a new taxonomy for progressive visualizations by systematically categorizing all PV publications that included visualizations with progressive features. Progressive visualizations can be categorized by well-known visualization taxonomies, but we also found that progressive visualizations can be distinguished by the way they manage their data processing, data domain, and visual update. Furthermore, we identified key properties such as uncertainty, steering, visual stability, and real-time processing that are significantly different with progressive applications. We also collected evaluation methodologies reported by the publications and conclude with statistical findings, research gaps, and open challenges.

Integrated health monitoring system for checking patients’ vitals

The increasing demand for efficient healthcare services has propelled the development of efficient health monitoring systems. This paper presents a comprehensive system designed to monitor the vital signs of patients in real-time, aimed at enhancing patient care and reducing the burden on healthcare providers. The system integrates various sensors to Data collected from these sensors are transmitted wirelessly to a central database, where it is processed and analyses using sophisticated algorithms. The results are accessible through a user-friendly interface for both healthcare professionals and patients. This health monitoring system offers several critical benefits. it provides real-time alerts for any abnormal vital signs, enabling prompt medical intervention. Secondly, the continuous monitoring allows for the early detection of potential health issues, which can significantly improve patient outcomes. The methodology involves is the analytical and systematic application of Arduino Nano Boards as microcontrollers, such as DS18B20, DHT11 and with WIFI Modula; which are driven- real time-based temperature frameworks for optimization and abstraction of accurate patient’s health data result. Results demonstrated the system's accuracy, reliability, and user satisfaction.

Monitoring and predicting spatio-temporal dynamics of river bankline movements: a case study for land use risk management in the lower Ganga River, India.

Changing the river course in the alluvial plain region is a common phenomenon that may have disastrous consequences. The risk of river bank erosion has increased dramatically during the last few decades. As a result, assessing the river bankline alteration is necessary. The study aims to determine the changes in the bankline in the lower Ganga River. This research presents a novel approach by using the digital shoreline analysis system (DSAS) in conjunction with geospatial data to monitor and predict long-term changes in river banks from 1965 to 2017, providing a comprehensive temporal analysis that is unprecedented in this study area. The study analyzes the bankline change along the river Ganga using DSAS using during the elapsed period. An erosion and accretion zonation was conducted based on the rate of bankline change of the river Ganga in the study area. The rate of bankline shifting was quantified using the endpoint rate (EPR) and linear regression rate (LRR) statistics computed using the DSAS model. The east bank of the Ganga in the study area experienced an average erosion of - 41.17m/year according to the LRR model. Whereas, the west bank eroded an average of - 2.32m/year between 1965 and 2017. 90.54% of the transect lines recorded erosion at the east bank and 53.69% of the transect lines at the west bank recorded erosion computed with LRR. For the assessment of the impact of river bankline change on the LULC of the study area, the future river banklines for 2027 and 2037 were forecasted. The result shows that by 2027 and 2037 about 133.24 and 147 km2 of agricultural land and 7.19 and 11.47 km2 of the built-up area may be affected by river bank erosion respectively. By extending the applications of DSAS and geospatial analytics to encompass predictive and impact assessment capabilities, this study significantly enriches the literature on the management of riverbank erosion and associated land use risks. This research provides important insights that improve river management and planning and enable the formulation of robust strategies to mitigate erosion risks on river banks.

Machine learning approach to investigate pregnancy and childbirth risk factors of sleep problems in early adolescence: Evidence from two cohort studies

BackgroundThis study aimed to predict early adolescent sleep problems using pregnancy and childbirth risk factors through machine learning algorithms, and to evaluate model performance internally and externally. MethodsData from the China Jintan Child Cohort study (CJCC; n=848) for model development and the US Healthy Brain and Behavior Study (HBBS; n=454) for external validation were employed. Maternal pregnancy histories, obstetric data, and adolescent sleep problems were collected. Several machine learning techniques were employed, including least absolute shrinkage and selection operator, logistic regression, random forest, naïve bayes, extreme gradient boosting, decision tree, and neural network. The area under the receiver operating characteristic curve, sensitivity, specificity, accuracy, and root mean square of residuals were used to evaluate model performance. ResultsKey predictors for CJCC adolescents’ sleep problems include gestational age, birthweight, duration of delivery, and maternal happiness during pregnancy. In HBBS adolescents, the duration of postnatal depressive emotions was the primary perinatal predictor. The prediction models developed in the CJCC had good-to-excellent internal validation performance but poor performance in predicting the sleep problems in HBBS adolescents. ConclusionThe identification of specific perinatal risk factors associated with adolescent sleep problems can inform targeted interventions during and after pregnancy to mitigate these risks. Health providers should consider integrating these predictive factors into routine pre- and postnatal assessments to identify at-risk populations. The variability in model performance across different cohorts highlights the need for context-specific models and the cautious application of predictive analytics across diverse populations. Future research should focus on refining predictive models to account for such variations, potentially through the incorporation of additional socio-cultural factors and genetic markers. This study emphasizes the importance of personalized and culturally sensitive approaches in the prediction and management of adolescent sleep problems, leveraging advanced computational methods to enhance maternal and child health outcomes.

Harnessing Educational Big Data Analytics for Decision-Making in Enhancing School Teaching Quality

The application of educational big data analytics holds significant importance in enhancing decision-making processes for school teaching quality. This study explores the effective utilization of educational big data analytics technologies to support the improvement of teaching quality in schools. Initially, the challenges and needs faced by current school teaching quality decision-making were analyzed, highlighting the critical role of educational big data analytics in this context. Subsequently, the limitations and gaps in existing study were identified through a review of related studies, underscoring the study value of this study. Based on this foundation, this study progresses through an examination of the decision-making factors that influence school teaching quality, problem description and model assumptions, construction of decision models, and model solutions using genetic algorithms. By analyzing key factors and constraints in the decision-making process for school teaching quality and integrating optimization algorithms, a viable decision support model was proposed and empirically analyzed. This study aims to provide a scientific basis for school administrators and decision-makers, thereby promoting continuous improvement in school teaching quality.

Optimized workflow of EV enrichment from human plasma samples for downstream mass spectrometry analysis

To improve the prognosis of bladder and prostate cancer, highly specific and sensitive biomarkers are needed for early detection, prognosis prediction, and therapeutic stratification. Extracellular vesicles (EV) from plasma could fill this gap due to their potential to serve as cancer biomarkers. However, the enrichment of EV is a major challenge, because the highly abundant plasma proteins are interfering with analytical downstream applications like mass spectrometry (MS). Therefore, the purity requirements of the EV samples must be carefully considered when selecting or developing a suitable EV enrichment method. The aim of this study was to compare a self-designed EV enrichment method based on density cushion centrifugation (DCC) combined with size exclusion chromatography (SEC) and concentration (method 1) with the exoRNeasy midi kit from Qiagen (method 2) and with unprocessed plasma. Furthermore, the single steps of method 1 were evaluated for their effectiveness to enrich EV from plasma. The results showed that the EV samples enriched with method 1 contained the highest levels of EV and exosome markers with simultaneously low levels of highly abundant plasma proteins. In summary, the combination of DCC, SEC and concentration proved to be a promising approach to discover EV-based biomarkers from plasma of cancer patients.

Molecularly Imprinted Microspheres in Active Compound Separation from Natural Product.

Molecularly Imprinted Microspheres (MIMs) or Microsphere Molecularly Imprinted Polymers represent an innovative design for the selective extraction of active compounds from natural products, showcasing effectiveness and cost-efficiency. MIMs, crosslinked polymers with specific binding sites for template molecules, overcome irregularities observed in traditional Molecularly Imprinted Polymers (MIPs). Their adaptability to the shape and size of target molecules allows for the capture of compounds from complex mixtures. This review article delves into exploring the potential practical applications of MIMs, particularly in the extraction of active compounds from natural products. Additionally, it provides insights into the broader development of MIM technology for the purification of active compounds. The synthesis of MIMs encompasses various methods, including precipitation polymerization, suspension polymerization, Pickering emulsion polymerization, and Controlled/Living Radical Precipitation Polymerization. These methods enable the formation of MIPs with controlled particle sizes suitable for diverse analytical applications. Control over the template-to-monomer ratio, solvent type, reaction temperature, and polymerization time is crucial to ensure the successful synthesis of MIPs effective in isolating active compounds from natural products. MIMs have been utilized to isolate various active compounds from natural products, such as aristolochic acids from Aristolochia manshuriensis and flavonoids from Rhododendron species, among others. Based on the review, suspension polymerization deposition, which is one of the techniques used in creating MIPs, can be classified under the MIM method. This is due to its ability to produce polymers that are more homogeneous and exhibit better selectivity compared to traditional MIP techniques. Additionally, this method can achieve recovery rates ranging from 94.91% to 113.53% and purities between 86.3% and 122%. The suspension polymerization process is relatively straightforward, allowing for the effective control of viscosity and temperature. Moreover, it is cost-effective as it utilizes water as the solvent.

Flood Exposure, Vulnerability, and Risk Distribution in Urban Areas: Application of Geospatial Data Analytics and Index Development

Over the past few decades, cities have experienced increased floods affecting property and threatening human life as a result of a warming planet. There is still an incomplete understanding of the flood risk patterns in urban communities with different socioeconomic characteristics. In this study, we produced separate flood exposure and vulnerability indices based on relevant factors, then combined them as a risk index for Houston, Texas and Charleston, West Virginia. We applied statistical methods to extract the most significant social vulnerability factors in each study area. Finally, we mapped significant hot spots or clusters of high flood risk and compared results to socioeconomically disadvantaged populations. Based on the results, high-risk or 1%-annual-chance floodplains cover 23% of the Houston and 7% of Charleston study areas. Within these floodplains, 13% of the total developed land in Houston and 9% in Charleston are situated. In the event of a 1%-annual-chance flood, an estimated 5% of the total population in Houston and 6% in Charleston may require evacuation. Statistically significant flood risk clusters could only be identified in Houston. The implications from this work help to provide an analysis framework for larger urban areas while offering suggestions for its improvement in smaller populated areas.

Pressure-driven formation of sub-femtoliter droplets in constricted T-shaped nanofluidic channel and application to isolation and counting of single nanoparticles

Nanofluidics has developed, enabling the analysis of single nanoparticles such as viruses and macromolecules using nanochannels. However, since nanoparticles migrate randomly by Brownian motion with displacements comparable to the nanochannel size, it is difficult to array and process nanoparticles individually in continuous phase flows with biological analyte concentrations of pM–nM. The present study developed a method to generate aqueous droplets of 102 aL–100 fL volumes in an organic phase in a nanochannel by pressure-driven flow control, and applied the method to the isolation of single nanoparticles with 101−102 pM concentrations. A T-shaped nanochannel with a narrow constriction section was proposed to locally enhance the shear force to form smaller droplets by 102 kPa external pressure, without increasing the pressure loss by downscaling the channel. The proposed channel design was validated, droplets with 0.8–1.0 fL volumes were formed with pL/min flow rates at Ca = 0.5–1.4 × 10−3, and the dynamics of droplet generation was well characterized by the linear scaling law. Using the developed device, encapsulation and counting of single fluorescent nanoparticles and single fluorescently-labeled DNA molecules in droplets were successfully demonstrated. This work will contribute to the development of droplet nanofluidics and ultrasmall analytical applications.

Physicochemical characterization and evaluation of polarity influence in eutectic systems applied to drug solubility

In the process of discovery and development of APIs (Active Pharmaceutical Ingredients), the rate of solubility or aqueous dissolution plays a very important role from the first evaluations of the candidates to the development of final marketable products. Since only the solubilized form of an active ingredient is capable of being transported and exerting its therapeutic effect across biological membranes, it is of utmost importance to control its solubility. However, the number of APIs under development with low water solubility has notoriously grown in recent years. Their pharmaceutical use will face problems such as insufficient bioavailability, late onset of action, and the need for large dosages.The present study aims to evaluate the behavior of ciprofloxacin and norfloxacin (fluoroquinolones), as model APIs for their different solubilities, in aqueous and non-aqueous natural deep eutectic solvents (NADES). The main objective is to improve the solubility of both drugs in pharmaceutical studies and analytical applications. In order to do so, two sets of eutectic solvents were selected, each composed with a polar solvent, an intermediate one, and a nonpolar solvent, as well. Very promising results were obtained in NADES of polar and intermediate polarity, for both drugs. In addition, the physicochemical characterization of such solvents was carried out to better understand the aforementioned results.

Target-triggered oxygen vacancy increase of Co3O4 nanoparticles with promoted peroxidase-like activity for specific turn-on colorimetric sensing of uranyl ions

As the most stable form of uranium in water and soil, uranyl ions (UO22+) possess strong biological toxicity and radioactivity, causing a great threat to human health. Therefore, it is of significance to develop reliable methods for monitoring uranyl ions in the environment. Here we propose a “light-up” colorimetric strategy based on target-accelerated peroxidase-mimicking activity of Co3O4 for the specific detection of uranyl ions. Original Co3O4 nanoparticles exhibit a certain peroxidase-like activity in catalyzing colorless 3,3′,5,5′-tetramethylbenzidine (TMB) to blue oxTMB with the participation of H2O2. When UO22+ is introduced, it interacts with the nanozyme rapidly and specifically and promotes the latter’s catalytic ability via increasing active oxygen vacancies on Co3O4 surface. According to such a phenomenon and mechanism, selective determination of UO22+ with favorable performance is achieved, with a linear measurement range of 0.2–10 μM and a detection limit of 0.08 μM. Reliability and practicability of the proposed method are demonstrated by analyzing the pollutant in several environmental water matrices. Our work not only offers a novel, efficient yet convenient approach for measuring UO22+, but may also inspire the exploration of new nanozyme-based sensing principles and strategies for other analytical applications.

SmartDampener: An Open Source Platform for Sport Analytics in Tennis

In this paper, we introduce SmartDampener, an open-source tennis analytics platform that redefines the traditional understanding of vibration dampeners. Traditional vibration dampeners favored by both amateur and professional tennis players are utilized primarily to diminish vibration transmission and enhance racket stability. However, our platform uniquely merges wireless sensing technologies into a device that resembles a conventional vibration dampener, thereby offering a range of tennis performance metrics including ball speed, impact location, and stroke type. The design of SmartDampener adheres to the familiar form of this accessory, ensuring that (i) it is readily accepted by users and robust under real-play conditions such as ball-hitting, (ii) it has minimal impact on player performance, (iii) it is capable of providing a wide range of analytical insights, and (iv) it is extensible to other sports. Existing computer vision systems for tennis sensing such as Hawk-eye and PlaySight, rely on hardware that costs millions of US dollars to deploy with complex setup procedures and is susceptible to lighting environment. Wearable devices and other tennis sensing accessories, such as Zepp Tennis sensor and TennisEye, using intrusive mounting locations, hinder user experience and impede player performance. In contrast, SmartDampener, a low-cost and compact tennis sensing device, notable for its socially accepted, lightweight and scalable design, seamlessly melds into the form of a vibration dampener. SmartDampener exploits opportunities in SoC and form factor design of conventional dampeners to integrate the sensing units and micro-controllers on a two-layer flexible PCB, that is bent and enclosed inside a dampener case made of 3D printing TPU material, while maintaining the vibration dampening feature and further being enhanced by its extended battery life and the inclusion of wireless communication features. The overall cost is $9.42, with a dimension of 21.4 mm × 27.5 mm × 9.7 mm (W × L × H) and a weight of 6.1 g and 5.8 hours of battery life. In proof of SmartDampener's performance in tennis analytics, we present various tennis analytic applications that exploit the capability of SmartDampener in capturing the correlations across string vibration, and racket motion, including the estimation of ball speed with a median error of 3.59 mph, estimation of ball impact location with accuracy of 3.03 cm, and classification of six tennis strokes with accuracy of 96.75%. Finally, extensive usability studies with 15 tennis players indicate high levels of social acceptance of form factor design for the SmartDampener dampener in comparison with alternative form factors, as well as its capability of sensing and analyzing tennis stroke in an accurate and robust manner. We believe this platform will enable exciting applications in other sports like badminton, fitness tracking, and injury prevention.

Simultaneous extraction of tocochromanols and flavan-3-ols from the grape seeds: Analytical and industrial aspects

Grape processing generates large amounts of by-products, including seeds rich in hydrophilic and lipophilic antioxidants. This study demonstrates, for the first time, that subjecting grape seeds to a single ultrasound-assisted extraction (UAE) with aqueous ethanolic solutions yields both flavan-3-ols and tocochromanols in the final extract. Notably, the water content in ethanol significantly influences the extractability of tocochromanols more than flavan-3-ols. Solid-to-solvent ratios of 1:50 to 1:2 were tested for both analytical and industrial applications. A sustainable analytical approach for recovering flavan-3-ols and tocochromanols using 60% and 96.4% ethanol extractions was validated and employed to profile nineteen genotypes of lesser-studied interspecific grape crosses (Vitis spp.). Different genotypes showed a wide range of concentrations of tocopherols (1.6–6.3 mg/100 g), tocotrienols (1.0–17.4 mg/100 g), and flavan-3-ols (861–9994 mg/100 g). This indicated that the genetic background and maturity of the plant material are crucial factors from an industrial perspective due to the initial concentration of bioactive compounds. Finally, the study also discussed the fundamental aspects of hydrophobic antioxidant extractability from the lipid matrix with aqueous ethanol solutions and the limitations of the workflow, such as the non-extractable tocochromanols and their esters and the losses of these lipophilic antioxidants during extraction.

Rapid Authentication of Medicinal Plants: Exploring the Applicability of Mass Spectrometry Imaging for Species Discrimination.

In the past few years, mass spectrometry imaging (MSI) has brought many new inspirations to plant research. However, current MSI experiments usually include only a single batch of samples, casting doubts on the reproducibility of phytochemical distribution across different batches. Consequently, MSI has seldom been applied to conduct species discrimination. In this experiment, MSI was employed to discriminate between two taxonomically similar plants, Scutellaria baicalensis Georgi and Scutellaria rehderiana Diels. A new concept termed a "spatial marker" was proposed in this article, which referred to the phytochemical marker that presented both intraspecies similarity and interspecies dissimilarity. Multiple batches of S. baicalensis and S. rehderiana were analyzed using MSI, proving that the authentication protocol using spatial markers was reliable and reproducible. The observed spatial markers were further identified using on-tissue tandem mass spectrometry and liquid chromatography coupled with mass spectrometry. Additionally, the spectral data collected from MSI were utilized to set up algorithm models for species discrimination. External validation confirmed that the established random forest model was extrapolated well to unknown samples. Overall, this investigation successfully explored the analytical applicability of MSI, facilitating rapid authentication of medicinal plants.

Long-term and Continuous Plasmonic Oligonucleotide Monitoring Enabled by Regeneration Approach.

The demand for continuous monitoring of biochemical markers for diagnostic purposes is increasing as it overcomes the limitations of traditional intermittent measurements. This study introduces a method for long-term, continuous plasmonic biosensing of oligonucleotides with high temporal resolution. Our method is based on a regeneration-based reversibility approach that ensures rapid reversibility in less than 1 minute, allowing the sensor to fully reset after each measurement. We investigated label-free and AuNP enhancements for different dynamic ranges and sensitivities, achieving a limit of detection down to pM levels. We developed a regeneration-based reversibility approach for continuous biosensing, optimizing buffer conditions using the Taguchi method to achieve rapid, consistent reversibility, ensuring reliable performance for long-term monitoring. We detected oligonucleotides in buffered and complex solutions, including undiluted and unfiltered human serum, for up to 100 sampling cycles in a day. Moreover, we showed the long-term stability of the sensor for monitoring capabilities in buffered solutions and human serum, with minimal signal value drift and excellent sensor reversibility for up to 9 days. Our method opens the door to new prospects in continuous biosensing by providing insights beyond intermittent measurements for numerous analytical and diagnostic applications.

Advances in LDI-MS Analysis: The Role of Chemical Vapor Deposition-Synthesized Silver Nanoparticles in Enhancing Detection of Low-Molecular-Weight Biomolecules.

In this investigation, we detail the synthesis of silver nanoparticles (AgNPs) via a precise chemical vacuum deposition (CVD) methodology, aimed at augmenting the analytical performance of laser desorption/ionization mass spectrometry (LDI-MS) for the detection of low-molecular-weight analytes. Employing a precursor supply rate of 0.0014 mg/s facilitated the formation of uniformly dispersed AgNPs, characterized by SEM and AFM to have an average diameter of 33.5 ± 1.5 nm and a surface roughness (Ra) of 11.8 nm, indicative of their homogeneous coverage and spherical morphology. XPS and SEM-EDX analyses confirmed the metallic silver composition of the nanoparticles with Ag peak splitting, reflecting the successful synthesis of metallic Ag. Comparative analytical evaluation with traditional MALDI matrices revealed that AgNPs significantly reduce signal suppression, thereby enhancing the sensitivity and specificity of LDI-MS for low-molecular-weight compounds such as triglycerides, saccharides, amino acids, and carboxylic acids. Notably, the application of AgNPs demonstrated a superior linear response for triglyceride signals with regression coefficients surpassing 0.99, markedly outperforming conventional matrices. The study further extends into quantitative analysis through nanoparticle-based laser desorption/ionization (NALDI), where AgNPs exhibited enhanced ionization efficiency, characterized by substantially lower limits of detection (LOD) and quantification (LOQ) for tested standards. Particular attention was paid to lipids with a detailed examination of their fragmentation pathways. These results highlight the significant potential of AgNPs synthesized via CVD to transform the analytical detection and quantification of low-molecular-weight compounds using NALDI. This approach offers a promising avenue for expanding the scope of analytical applications in mass spectrometry and introducing innovative methodologies for enhanced precision and sensitivity.

Standardized Electric-Field-Resolved Molecular Fingerprinting.

Field-resolved infrared spectroscopy (FRS) of impulsively excited molecular vibrations can surpass the sensitivity of conventional time-integrating spectroscopies, owing to a temporal separation of the molecular signal from the noisy excitation. However, the resonant response carrying the molecular signal of interest depends on both the amplitude and phase of the excitation, which can vary over time and across different instruments. To date, this has compromised the accuracy with which FRS measurements could be compared, which is a crucial factor for practical applications. Here, we utilize a data processing procedure that overcomes this shortcoming while preserving the sensitivity of FRS. We validate the approach for aqueous solutions of molecules. The employed approach is compatible with established processing and evaluation methods for the analysis of infrared spectra and can be applied to existing spectra from databases, facilitating the spread of FRS to new molecular analytical applications.

Exploring Excited State Landscapes with Surface Enhanced Hyper-Raman Spectroscopy.

In this Perspective, we provide a historical overview of the surface-enhanced hyper-Raman scattering (SEHRS) effect, describe its essential components, highlight the close connection between theory and experiment in several vignettes, and discuss recent analytical applications. SEHRS, the two-photon analog of surface-enhanced Raman scattering (SERS), is a spontaneous nonlinear scattering exhibited by molecules in a plasmonic field. Hyper Raman provides distinctive information on the molecular vibrations and electronic excited states of analytes. A 40-year old mystery surrounding the SEHRS spectra of R6G is used to illustrate the power of SEHRS to explore excited electronic states, revealing how non-Condon effects can influence the two-photon molecular properties. The exceptionally large enhancement factors (>1013) obtained from SEHRS enable the analysis of single molecules and molecules at very low concentrations. This high sensitivity is further augmented by an increased sensitivity to chemical effects, allowing SEHRS to probe changes in the local environment and the orientation of surface ligands. As most SEHRS experiments employ near-infrared (NIR) and short-wave infrared (SWIR) light, it also holds promise for bioimaging studies. Before concluding, we discuss future directions and challenges for the field as it moves forward.