Identification Of Substances Research Articles

A d10-Cd Cluster Containing Sandwich-Type Arsenotungstate Exhibiting Fluorescent Recognition of Carcinogenic Dye in Methanol.

A d10-Cd cluster containing sandwich-type arsenotungstate [C3H12N2]6[Cd4Cl2(B-α-AsW9O34)2] was synthesized and its structure characterized through elemental analyses, X-ray powder diffraction (XRPD), IR spectroscopy, X-ray photoelectron spectroscopy (XPS), and single-crystal X-ray diffraction. The X-ray analysis revealed that the molecular unit of the compound consists of a captivating tetra-Cd-substituted sandwich-type polyoxoanion, accompanied by six elegantly protonated 1,2-diaminopropane as counter ions. The further novelty of the tetranuclear cadmium cluster lies in its occupied chlorine atom sites. This makes it highly susceptible to coordinate reactions with nitrogen on polycyclic aromatic hydrocarbons, thereby exhibiting different fluorescent signals that facilitate the identification and detection of these carcinogenic substances in methanol.

Green Synthetic Methods of Oxazine and Thiazine Scaffolds as Promising Medicinal Compounds: A Mini-review.

Medical researchers have paid close attention to the green synthesis of oxazine and thiazine derivatives since they provided a lead molecule for the creation of numerous possible bioactive compounds. This review provides more information on green synthesis, which will be very helpful to researchers in creating the most effective, affordable, and clinically significant thiazine and oxazine derivatives that are anticipated to have strong pharmacological effects. This has resulted in the identification of several substances with a wide range of intriguing biological functions. This article's goal is to examine the numerous green chemical processes used to create oxazine and thiazine derivatives and their biological activity. We anticipate that researchers interested in oxazine and thiazine chemicals will find this material to be useful. We anticipate that medicinal chemists looking for new active medicinal components for drug discovery and advance progress will find this review of considerable interest.

Portable near-infrared detection to replace color tests in an analytical scheme for forensic drug identification

In the ever-changing drug market the popularity and availability of substances is dynamic. In the Netherlands, ketamine and various cathinones have recently seen increased prevalence. It is crucial for law enforcement to quickly obtain an initial indication of the identity of a substance. This first test also serves as quality control for subsequent confirmation with GC–MS analysis. Traditionally, color tests have been used for these purposes. While these tests are quick and inexpensive, they have the disadvantage of reacting only to a few traditional drugs. Suitable color tests are not available for many new psychoactive substances (NPS). Near-infrared (NIR) spectroscopy is a rapid technique that provides a characteristic spectrum for organic compounds. This technique is more versatile than color tests and can adapt more quickly to market changes by incorporating reference spectra into the library. This study demonstrates the feasibility of obtaining a good quality NIR spectrum from a 20 mg sub-sample in a test tube. This was achieved by scanning the test tube through the glass bottom. In the routine analytical scheme, these test tubes were subsequently batchwise analyzed by GC–MS. From the NIR spectra, 84 % true positive and 100 % true negative results were achieved on 516 casework samples, including identification of substances without available color tests like ketamine. Missed false negatives primarily involved new substances absent from the library, emphasizing the need for continuous library updates. NIR’s adaptability to market changes is crucial, allowing the inclusion of new substances as they emerge. This method enhances law enforcement’s ability to make informed decisions, aiding in the indictment process.

Non-targeted screening and toxicity study of safety risk substances in facial skincare products: Molecular networking and computational toxicology strategy

BackgroundRisk substances in cosmetics have long been associated with adverse reactions. However, as risk substances become more concealed and diversified, conventional targeted analysis methods are no longer sufficient to meet regulatory requirements. ObjectiveTo construct a rapid and effective non-targeted screening method for the identification of risk substances, and to provide a high-throughput method for toxicity assessment. MethodsMolecular networking was utilized for the non-targeted screening of risk substances in facial skincare products, and the toxicity of these risk substances was evaluated through molecular docking and quantitative structure-activity relationship (QSAR) models. ResultsThrough molecular networking, we identified seven known prohibited ingredients, six of which were confirmed using standard substances. In addition, 17 potential risk substances were discovered within molecular clusters containing prohibited ingredients, including antibiotics, antihistamines, and phthalates, etc. Notably, chloramphenicol base and N-desmethyl chlorpheniramine exhibited stronger binding affinity to keratin 5/14 than chloramphenicol and chlorpheniramine through molecular docking, respectively. Additionally, toxicity prediction results indicated that the carcinogenicity of antibiotics presented gender differences in mice and rats, and two chlorpheniramine derivatives also showed carcinogenicity in mice. Moreover, of the 24 compounds, 11 showed skin sensitization, while 14 caused skin irritation. Furthermore, half of these compounds demonstrated potential developmental toxicity, and only 4-nitrobenzenethiol was found to be mutagenic. ConclusionIn this study, we developed a visualization strategy for non-targeted screening of risk substances and a high-throughput method for initial toxicity assessment of risk substances.

From complexity to clarity: development of CHM-FIEFP for predicting effective components in Chinese herbal formulas by using big data.

The presence of complex components in Chinese herbal medicine (CHM) hinders identification of the primary active substances and understanding of pharmacological principles. This study was aimed at developing a big-data-based, knowledge-driven in silico algorithm for predicting central components in complex CHM formulas. Network pharmacology (TCMSP) and clinical (GEO) databases were searched to retrieve gene targets corresponding to the formula ingredients, herbal components, and specific disease being treated. Intersections were determined to obtain disease-specific core targets, which underwent further GO and KEGG enrichment analyses to generate non-redundant biological processes and molecular targets for the formula and each component. The ratios of the numbers of biological and molecular events associated with a component were calculated with a formula, and entropy weighting was performed to obtain a fitting score to facilitate ranking and improve identification of the key components. The established method was tested on the traditional CHM formula Danggui Sini Decoction (DSD) for gastric cancer. Finally, the effects of the predicted critical component were experimentally validated in gastric cancer cells. An algorithm called Chinese Herb Medicine-Formula vs. Ingredients Efficacy Fitting & Prediction (CHM-FIEFP) was developed. Ferulic acid was identified as having the highest fitting score among all tested DSD components. The pharmacological effects of ferulic acid alone were similar to those of DSD. CHM-FIEFP is a promising in silico method for identifying pharmacological components of CHM formulas with activity against specific diseases. This approach may also be practical for solving other similarly complex problems. The algorithm is available at http://chm-fiefp.net/.

Double Designers: Detection of Bromazolam and Metonitazene in Postmortem Casework.

The identification of novel psychoactive substances (NPSs) in casework at the Los Angeles County Department of Medical Examiner (LACDME) is constantly evolving. The case detailed herein marks the first detection of metonitazene in forensic casework at the LACDME which occurred in August 2023. Furthermore, bromazolam was found in the decedent's system and both substances were identified in drug evidence collected at the death scene. No other drugs were detected and the manner and cause of death were determined as accidental due to effects of bromazolam and metonitazene. The concentrations detected were 1.6 ng/mL and 4.4 ng/mL of metonitazene in the jugular blood and femoral blood, respectively, and 93 ng/mL of bromazolam in the femoral blood. Constraints in screening techniques conducted by toxicology laboratories create challenges in which numerous NPSs available on the illicit drug market can go undetected. Even if labs detect an NPS in their screening methodology, confirmation methods might not cover every NPS, given the impracticality of labs keeping pace with validations as new NPSs emerge in casework. The significance of testing medical evidence collected at death scenes by drug chemistry analysis becomes crucial when initial toxicology results are negative. In cases where there is evidence of potential drug paraphernalia this is especially true as it can be pivotal in determining the cause and manner of death.

Phytochemical Profiling and Nutraceutical Benefits of Methanol Leaf Extracts from Datura alba ness

Introduction: Species of the Datura plant have demonstrated significant pharmacological benefits in combating various diseases over the past decade. However, little information is available on Datura alba ness (DAN), and no studies have yet reported its proximate composition or characterized its phytochemical components. This study aims to investigate the nutritional, phytochemical and bioactive properties of Datura alba ness. Materials and Methods: Fresh leaves of Datura alba ness were collected from Abua Odua local government area of Rivers State, Nigeria and properly identified and authenticated. The fresh leaves were subjected to proximate analysis using standard protocols while the dried leaves were extracted using methanol by the method of cold maceration to obtain methanolic extract of Datura alba ness (MEDAN). The MEDAN was further subjected to phytochemical screening to obtain the various phytochemicals and Gas chromatography and mass spectrometry (GC-MS) analysis for detailed identification and characterization of the various bioactive compounds. Results: Proximate analysis of fresh leaves of DAN showed the presence of moisture (60.4%), crude protein (21.9%), carbohydrate (7.6%), crude fibre (4.77%), ash (4.17%) and fat (1.19%) while the phytochemical screening revealed the presence of flavonoids (49.14%), alkaloids (19.16%), tannins 1(12.19%), saponins (11.72%), phenols (5.33%), cardiac glycosides (1.91%), oxalates (0.43%) and phytates (0.43%). Also, the GC-MS analysis of MEDAN identified twenty-nine (29) active compounds with four (4) observed as the most abundant (with area concentration >10%). They include Bis (2-ethylhexyl) phthalate (15.97%), 13-hexyloxacyclotridec-10-en-2-one (14.26%), n-hexadecanoic acid (12.87%) and Gamma.-Sitosterol (10.77). Conclusion: The identification of various nutrients, compounds, and bioactive substances through proximate, phytochemical, and GC-MS analysis of fresh leaves and methanol extract of Datura alba ness highlights its potential as a supplement and therapeutic agent. These findings suggest that Datura alba ness contains numerous bioactive compounds that may offer effective treatments for conditions related to oxidative stress, nervous system disorders, tissue damage, inflammation, and bacterial infections.

Identification of polar bioactive substances in the Upper Rhine using effect-directed analysis

Effect-Directed Analysis (EDA) was used to identify bioactive compounds in surface and well water from the Upper Rhine, and to evaluate their properties against the criteria set for Persistent, Mobile and Toxic (PMT) and very persistent and very mobile (vPvM) substances. A multi-layered solid-phase extraction was implemented to enrich a broad range of polar substances from the collected samples. The extracts were fractionated into 108 fractions and tested in the transthyretin (TTR)-binding assay measuring displacement of fluorescently labeled thyroxine (FITC-T4 TTR-binding assay) and the Aliivibrio fischeri bioluminescence (AFB) bioassay. Bioactive fractions guided the identification strategy using high-resolution mass spectrometry. Chemical features were systematically annotated using library databases and suspect lists, incorporating an automated assessment of the quality of each annotation. Based on this assessment, each chemical feature was assigned a specific identification confidence level. Identification of bioactive compounds was facilitated by using bioassay specific suspect lists that were extracted from an in-house developed database of positive and negative TTR-binding compounds and from a recently published database of active inhibitors of AFB. This resulted in the identification and confirmation of ten bioactive substances, including four evaluated as PMT and vPvM substances (diclofenac, trifloxystrobin acid, 6:2 FTSA and PFOA), and one as a potential PMT substance (4-aminoazobenzene). This study demonstrates the effectiveness of EDA in the identification of PMT/vPvM substances in the aquatic environment, facilitating their prioritization for comprehensive environmental risk assessment and possible regulation.

Quantitation of Usnic acid from three Usnea species, its commercial formulations Using High-Performance Thin-Layer Chromatography-Mass Spectrometry (HPTLC-MS) and their metabolite profiling using UPLC-QTof-MSE.

The genus Usnea (Parmeliaceae; lichenized Ascomycetes) is pale grayish-green fruticose lichens which grow as leafless mini-shrubs and comprise about 360 species. Most of the Usnea species are edible and is utilized in preparation of traditional foods as well as in medicines to combat wide range of ailments. The goal of this work was to quantify usnic acid in three Usnea spp. [Usnea ghattensis (UG), Usnea orientalis (UO) and Usnea undulata (UU)] using HPTLC-MS and chemical profiling of acetone extracts using UPLC-QTof-MSE resulted in the identification of sixteen compounds based on their MS/MS fragmentation patterns. Hyphenated techniques, HPTLC-MS and UPLC-QTof-MSE have been proposed to quantify usnic acid and analysis of metabolites in the crude extracts qualitatively. This method allowed tentative characterization of metabolites from Usnea spp. The quantification study showed the excellent linearity of the usnic acid at 0.25-1 µg/band with a correlation coefficient r 2>0.99, and LOD, LOQ was found to be 51.7 and 156.6 ng/band, respectively. Further, UPLC-QTof-MSE analysis of crude extract led identification of lichen substances through their exact molecular masses and MS/MS fragmentation studies. The present study summarizes HPTLC method for quantification of usnic acid in three different Usnea spp. Along with two herbal formulations containing Usnea spp. as the ingredient and developed method was validated as per the ICH guidelines and further UPLC-QTof-MSE analysis provides characterization of the sixteen different secondary metabolites based on their mass fragmentation studies. Rapid HPTLC method for quantification of usnic acid in three different Usnea spp. along with two herbal formulations and metabolite profiling using UPLC-QTof-MSE.

TexSe1-x Shortwave Infrared Photodiode Arrays with Monolithic Integration.

TexSe1-x shortwave infrared (SWIR) photodetectors show promise for monolithic integration with readout integrated circuits (ROIC), making it a potential alternative to conventional expensive SWIR photodetectors. However, challenges such as a high dark current density and insufficient detection performance hinder their application in large-scale monolithic integration. Herein, we develop a ZnO/TexSe1-x heterojunction photodiode and synergistically address the interfacial elemental diffusion and dangling bonds via inserting a well-selected 0.3 nm amorphous TeO2 interfacial layer. The optimized device achieves a reduced dark current density of -3.5 × 10-5 A cm-2 at -10 mV, a broad response from 300 to 1700 nm, a room-temperature detectivity exceeding 2.03 × 1011 Jones, and a 3 dB bandwidth of 173 kHz. Furthermore, for the first time, we monolithically integrate the TexSe1-x photodiodes on ROIC (64 × 64 pixels) with the largest-scale array among all TexSe1-x-based detectors. Finally, we demonstrate its applications in transmission imaging and substance identification.

Research on wheat impurity identification method based on terahertz imaging technology

The traditional detection of impurities in wheat has difficulties such as low precision, time-consuming, and cumbersome, therefore, it is important to study the method of rapid and accurate detection of impurities in wheat for correctly assessing the quality grade of wheat. Terahertz (THz) technology has many superior properties such as transient, broadband, low-energy, and penetrating, which can realize rapid and nondestructive detection of wheat quality. In this study, a classification and recognition algorithm AHA-RetinaNet-X for wheat impurity terahertz images based on RetinaNet and Artificial hummingbird algorithm (AHA) is proposed.A THz three-dimensional tomography imaging system is used to image wheat and its impurities, and two THz image datasets, respectively the wheat and impurity dataset for verifying the classification effect of wheat and impurities and the impurity dataset for verifying the classification effect of impurities. The experimental results show that the AHA-RetinaNet-X model outperforms other detection and classification models in terms of accuracy, F1-score, precision, recall, and specificity, and is able to achieve 96.1%, 94.9%, 95.2%, 95.8%, 95.5%, 95.3%, and 93.3% for the wheat and impurity dataset and the impurity dataset, respectively, 95.6%, 96.3%, and 95.2%, and the mAP value of AHA-RetinaNet-X is also higher than the other models and can reach 92.1%. The combination of THz imaging technology and AHA-RetinaNet-X can realize the classification and identification of wheat and impurities, which provides a new method for the non-contact rapid nondestructive detection and identification of wheat and impurities, and also provides a reference for the research of the identification and detection methods of other substances.

Risk substance identification of asphalt VOCs integrating machine learning and network pharmacology

Asphalt releases volatile organic compounds (VOCs) during paving processes, posing risks to workers and the environment. The complex composition of asphalt and the evolving of VOCs present challenges in accurately assessing their potential environmental and health impacts using traditional experimental approaches. This study aimed to develop a robust computational framework integrating machine learning and network pharmacology to predict the risks from the asphalt VOCs. The results show that the MACCS+XGBoost model achieved the highest predictive performance, with an accuracy of 0.85, balanced accuracy of 0.84, sensitivity of 0.83, specificity of 0.84, and F1-score of 0.84 in the external validation. The network pharmacology analysis revealed that the identified VOCs with reproductive toxicity potential may disrupt key processes such as spermatogenesis, ovarian function, and hormonal regulation, providing mechanistic insights into their potential impacts. This advancement supports a proactive approach to environmental protection and fosters the transition towards a more sustainable, low-carbon transportation.

UPLC-MS/MS analysis and chemical profile of Ficus pertusa L. bark, an ethnomedicinal resource from intertropical America.

Background Ficus pertusa is a native species from intertropical America used for medicinal purpouses. However, despite its importance as ethnobotanical resource, there is poor chemical information about its extracts. Therefore, the objective of this study was to evaluate the phytochemical profile of the hydroalcoholic extract of F. pertusa barks through UPLC-MS/MS, as well as its phenolic and flavonoid content. Methodology The chemical identification was performed by Ultra-High Liquid Chromatography coupled to Mass Spectrometry, whereas the Total Phenolic content and Total Flavonoid quantification were measured through UV spectrometry. Results As a result, 19 substances were identified, which are mostly included within the group of condensed tannins, phenolic acids and flavonoids, such as procyanidins, chlorogenic acid and luteolin. Conclusion The identification of the main substances of F. pertusa’s bark and the measurement of its polyphenols content can guide the understanding of the pharmacological mechanisms involved in its ethnomedicinal uses.

Identification of Bioactive Substances Derived from the Probiotic-Induced Bioconversion of Lagerstroemia speciosa Pers. Leaf Extract That Have Beneficial Effects on Diabetes and Obesity.

Lagerstroemia speciosa L. (Banaba) has been used as a functional food because of its diuretic, decongestant, antipyretic, anti-hyperglycemic, and anti-adipogenic activities. Triterpene acids, including corosolic acid, oleanolic acid, and asiatic acid, are the principal phytochemicals in Banaba and are potentially anti-diabetic substances, owing to their effect on blood glucose concentration. Bioconversion of Banaba leaf extract (BLE) by Lactobacillus plantarum CBT-LP3 improved the glucose uptake, insulin secretion, and fat browning of this functional food. Furthermore, we identified asiatic acid, which was found to be increased by 3.8-fold during the L. plantarum CBT-LP3-mediated bioconversion process using metabolite profiling. Most previous studies have focused on corosolic acid, another triterpene acid that is a known anti-diabetic compound and is used to standardize BLE preparations. However, asiatic acid is the second most common of the triterpene acids and is also well known to have anti-diabetic properties. The present study has provided strong evidence that asiatic acid represents an alternative to corosolic acid as the most important active compound. These results suggest that the probiotic-mediated bioconversion of BLE may improve the anti-diabetic effects of this functional food. This implies that the consumption of a probiotic should be encouraged for people undergoing BLE treatment to improve its anti-diabetic effects.

The Effect of the Extraction Conditions on the Antioxidant Activity and Bioactive Compounds Content in Ethanolic Extracts of Scutellaria baicalensis Root.

Ethanolic extracts of Baikal skullcap (Scutellaria baicalensis) root were obtained using various techniques, such as maceration, maceration with shaking, ultrasound-assisted extraction, reflux extraction, and Soxhlet extraction. The influence of the type and time of isolation technique on the extraction process was studied, and the quality of the obtained extracts was determined by spectrophotometric and chromatographic methods to find the optimal extraction conditions. Radical scavenging activity of the extracts was analyzed using DPPH assay, while total phenolic content (TPC) was analyzed by the method with the Folin-Ciocalteu reagent. Application of gas chromatography with mass selective detector (GC-MS) enabled the identification of some bioactive substances and a comparison of the composition of the particular extracts. The Baikal skullcap root extracts characterized by both the highest antioxidant activity and content of phenolic compounds were obtained in 2 h of reflux and Soxhlet extraction. The main biologically active compounds identified in extracts by the GC-MS method were wogonin and oroxylin A, known for their broad spectrum of biological effects, including antioxidant, anti-inflammatory, antiviral, anticancer, and others.

Effect of argon on classification and identification of organics based on laser‐induced breakdown spectroscopy

AbstractOrganic matter is the material basis of life, which is widely present in people's products and lives. The rapid and accurate detection of organic matter can be applied to the identification of fake and inferior products, the traceability of the origin of agricultural products, and the early warning of explosives in antiterrorism. By combining laser‐induced breakdown spectroscopy (LIBS) with argon, the spectral characteristics of five common organic compounds (nitroglycerin, metronidazole, polyformaldehyde, polypropylene, and phenolic resin), the plasma lifetime, the parameters of plasma thermodynamic state, and the distribution of three existing forms of the element C in two gas environments were compared, the influence of argon on organic LIBS results and the reasons for its enhancement were analyzed. The results indicate that argon makes the atomization of carbon chain structures in organic matter more complete, and reduce the interference of the element N in air on the organic LIBS results to a certain extent. The intensity differences of different organic substances are more obvious. support vector machine optimized by principal component analysis and particle swam optimization algorithm was used to classify five kinds of organic matter. The prediction accuracy of classification in air is 94.4%, and it improves to 100% in argon. This result provides a powerful method for high‐precision, real‐time in‐situ, and rapid identification of organic substances, which has important scientific significance for the study of organic substances LIBS.

P04-04 Development of a Putative Adverse Outcome Pathway (AOP) for the identification of substances with a potential link to Alzheimer's Disease (AD)

P04-04 Development of a Putative Adverse Outcome Pathway (AOP) for the identification of substances with a potential link to Alzheimer's Disease (AD)

Flavor identification based on olfactory-taste synesthesia model and hybrid convolutional neural network-random forest

Abstract The bionic-based electronic nose (e-nose) and electronic tongue (e-tongue) show satisfactory performance in flavor analysis. Traditional flavor analysis of the e-nose and e-tongue systems focuses on data fusion, and the effects of the bionic characteristics on the flavor analysis performance are rarely studied. Motivated by this, a method, including an olfactory-taste synesthesia model (OTSM) and a convolutional neural network-random forest (CNN-RF), is proposed for the effective identification of flavor substances. The OTSM is developed for human nerve conduction mechanisms to enhance the bionic characteristics of the e-nose and e-tongue systems and is combined with a CNN-RF model for flavor identification. The results show that, first, when stimulated by e-nose and e-tongue data, physiological 1/f characteristics and synchronization are shown using the OTSM. The enhancement effects on the bionic characteristics of the fusion system are validated using the 1/f characteristics and synchronization. Second, the fully connected layer for the CNN is replaced by RF to improve the identification performance of flavor substances. Finally, CNN-RF is evaluated in comparison with other flavor recognition models and ablation studies to confirm its effectiveness. By comparison, the best recognition performance, including the accuracies of 96.67%, 96.67%, and 95.00%, the F1-scores of 96.65%, 96.66%, and 94.95%, and the kappa coefficients of 96.03%, 96.10%, and 93.44%, for five beers, five apples, and four mixed solutions, respectively, is obtained by CNN-RF. In conclusion, excellent flavor identification for the fusion system is achieved using the OTSM and CNN-RF models.

Feature-Based Molecular Network for New Psychoactive Substance Identification: The Case of Synthetic Cannabinoids in a Seized e-Liquid and Biological Samples.

The comprehensive detection of new psychoactive substances, including synthetic cannabinoids along with their associated metabolites in biological samples, remains an analytical challenge. To detect these chemicals, untargeted approaches using appropriate bioinformatic tools such as molecular networks are useful, albeit it necessitates as a prerequisite the identification of a node of interest within the cluster. To illustrate it, we reported in this study the identification of synthetic cannabinoids and some of their metabolites in seized e-liquid, urine, and hair collected from an 18-year-old poisoned patient hospitalized for neuropsychiatric disorders. A comprehensive analysis of the seized e-liquid was performed using gas chromatography coupled with electron ionization mass spectrometry, 1H NMR, and liquid chromatography coupled with high resolution tandem mass spectrometry combined with data processing based on molecular network strategy. It allowed researchers to detect in the e-liquid known synthetic cannabinoids including MDMB-4en-PINACA, EDMB-4en-PINACA, MMB-4en-PINACA, and MDMB-5F-PICA. Compounds corresponding to transesterification of MDMB-4en-PINACA with pentenol, glycerol, and propylene glycol were also identified. Regarding the urine sample of the patient, metabolites of MDMB-4en-PINACA were detected, including MDMB-4en-PINACA butanoic acid, dihydroxylated MDMB-4en-PINACA butanoic acid, and glucurono-conjugated MDMB-4en-PINACA butanoic acid. Hair analysis of the patient allowed the detection of MDMB-4en-PINACA and MDMB-5F-PICA in the two investigated hair segments. This untargeted analysis of seized materials and biological samples demonstrates the utility of the molecular network strategy in identifying closely related compounds and metabolites of synthetic cannabinoids. It also emphasizes the need for developing strategies to anchor molecular networks, especially for new psychoactive substances.

Recent improvements in the novel approach to the fast detection of surface contaminations

Here, we present the idea behind a novel measurement system and the recent improvements in detecting surface contaminations using a tunable, very fast three-core quantum cascade laser (QCL) system. It is developed as a potential replacement for the old established double-wheel sampling system currently used to detect surface threats caused by hazardous, non-volatile chemical substances. The novel system design is based on an optical measurement principle, allowing standoff detection and identification, thus rendering ground contact unnecessary. One of the leading military requirements for a CBRN reconnaissance vehicle is a high marching speed; hence, this new system is designed to have an ultra-short acquisition time and an adequate spectral bandwidth for measurements in the mid-infrared (MIR). This innovative approach may replace the more cumbersome indirect detection techniques. We discuss the main advantages and evaluate possible application scenarios that we expect from this setup. Active IR backscatter spectroscopy generally permits the contactless identification of hazardous chemical substances on surfaces. Here, the design was optimized around an approximate measuring distance of decimeters. Using a monoaxial setup, the autofocus is less demanding than alternative approaches and even allows for rapid measurements of rough surfaces. We built the improved measurement system based on novel QCL modules that integrate micro-opto-electro-mechanical grating scanners (MOEMS) in an external cavity. These elaborate laser modules provide almost one kilohertz (kHz) spectral scan speeds and typically cover a spectral range of 200–300 cm-1, respectively. Furthermore, such modules achieve measurement times of as short as one millisecond per spectrum. The full spectral coverage of the system is realized by coupling several of such modules.