Raman Sensor Research Articles

Process analytical technology based quality assurance of API concentration and fiber diameter of electrospun amorphous solid dispersions

In this study, a novel quality assurance system was developed utilizing Process analytical technology (PAT) tools and artificial intelligence (AI). Our goal was to monitor the critical quality attributes (CQAs) like drug concentration, morphology and fiber diameter of electrospun amorphous solid dispersion (ASD) formulations with fast at-line techniques. Doxycycline-hyclate (DOX), a tetracycline-type antibiotic was used as a model drug with 2-hydroxypropyl-β-cyclodextrin (HP-β-CD) as the matrix excipient. The water-based formulations were electrospun with high-speed electrospinning (HSES). Raman and NIR sensors and machine vision-based color measurement techniques were employed to accurately determine the drug concentration. Given that morphology can influence the solubility of the drug, a convolutional neural network (CNN)-based AI model was developed to examine this property and detect manufacturing defects. Additionally, the diameter of electrospun fibrous samples was measured using camera images and a trained AI model, enabling rapid analysis of fiber diameter with results similar to that of scanning electron microscopy (SEM). These methods and models demonstrate potential in-line analytical tools, offering rapid, cheap and non-destructive analysis of ASD formulations.

The Study of the Behavior of CaO-SiO2-Al2O3-Na2O-Based Mold Flux in 1,400ºC by a Fiber-Optic Raman Sensor

The Study of the Behavior of CaO-SiO2-Al2O3-Na2O-Based Mold Flux in 1,400ºC by a Fiber-Optic Raman Sensor

MitoBADY-based Raman sensing of neutrophil-like cells

The aim was to evaluate the potential of MitoBADY as a Raman sensor in monitoring the drug-induced differentiation of promyelocytic cells into neutrophils. Neutrophils are important immune system components, and their metabolic disorders lead to serious diseases. Primary neutrophils found in the bloodstream are short-lived cells, terminally differentiated, and unable to proliferate. Thus, the characteristics of induced differentiation of promyelocytic cells are valuable for better chemotherapy design, as it is difficult to conduct such studies in vivo or ex vivo in bone marrow environments. Alternatively, a stable in vitro model can be used to analyze the differentiation process, e.g., the HL-60 promyelocytic leukemia cell line. The standard method to confirm the promyelocytic differentiation is immunophenotyping, which is complex and laborious. Here, we propose a simple spectroscopic-based alternative that uses the Raman signal of the MitoBADY sensor to identify neutrophil-like cells. We found that the ratio of the intensity of three bands, i.e., A= (I750+I2220)/I750, B=(I2850+I2220)/I2850, and A/B, of the Raman spectra of cells incubated with the MitoBADY is a specific marker indicating the induction of neutrophil differentiation of HL-60 cells. Flow cytometry, measuring the level of the expression of the CD11b surface protein, was used as a reference method. Raman measurements allowed the classification of neutrophil-like cells with a sensitivity of 79.8 % and a specificity of 79.5 %. These values demonstrate the great potential of the proposed methodology as a rapid and reliable technique for the evaluation of the differentiation of promyelocytic cells into neutrophils.

Surface-enhanced Raman sensor with molecularly imprinted nanoparticles as highly sensitive recognition material for cancer marker amino acids

Surface-enhanced Raman scattering (SERS) is a powerful technique primarily due to its high sensitivity and signal-enhancing properties, which enable the identification of unique vibrational fingerprints. These fingerprints can be used for the diagnosis and monitoring of diseases such as cancer. It is crucial to selectively identify cancer biomarkers for early diagnosis. A correlation has been established between the reduction in the concentration of specific amino acids and the stage of the disease, particularly tryptophan (TPP) and tyrosine (TRS) in individuals diagnosed with prostate cancer. In this work, we present a strategy to analyze TPP and TRS amino acids using molecularly imprinted polymer nanoparticles (nanoMIPs), which selectively detect target molecules in a SERS sensor. NanoMIPs are synthesized using the solid-phase molecular imprinting method with TPP and TRS as templates. These are then immobilized on a SERS substrate with gold nanoparticles to measure samples prepared from tryptophan and tyrosine in phosphate-buffered saline. The detection and quantification limits of the designed sensor are 7.13 μM and 23.75 μM for TPP, and 22.11 μM and 73.72 μM for TRS, respectively. Our study lays the groundwork for future investigations utilizing nanoMIPs in SERS assessments of TPP and TRS as potential biomarkers for prostate cancer detection.

Chemically Functionalized 2D Transition Metal Dichalcogenides for Sensors.

The goal of the sensor industry is to develop innovative, energy-efficient, and reliable devices to detect molecules relevant to economically important sectors such as clinical diagnoses, environmental monitoring, food safety, and wearables. The current demand for portable, fast, sensitive, and high-throughput platforms to detect a plethora of new analytes is continuously increasing. The 2D transition metal dichalcogenides (2D-TMDs) are excellent candidates to fully meet the stringent demands in the sensor industry; 2D-TMDs properties, such as atomic thickness, large surface area, and tailored electrical conductivity, match those descriptions of active sensor materials. However, the detection capability of 2D-TMDs is limited by their intrinsic tendency to aggregate and settle, which reduces the surface area available for detection, in addition to the weak interactions that pristine 2D-TMDs normally exhibit with analytes. Chemical functionalization has been proposed as a consensus solution to these limitations. Tailored surface modification of 2D-TMDs, either by covalent functionalization, non-covalent functionalization, or a mixture of both, allows for improved specificity of the surface-analyte interaction while reducing van der Waals forces between 2D-TMDs avoiding agglomeration and precipitation. From this perspective, we review the recent advances in improving the detection of biomolecules, heavy metals, and gases using chemically functionalized 2D-TMDs. Covalent and non-covalent functionalized 2D-TMDs are commonly used for the detection of biomolecules and metals, while 2D-TMDs functionalized with metal nanoparticles are used for gas and Raman sensors. Finally, we describe the limitations and further strategies that might pave the way for miniaturized, flexible, smart, and low-cost sensing devices.

Genetic algorithm-based semisupervised convolutional neural network for real-time monitoring of Escherichia coli fermentation of recombinant protein production using a Raman sensor.

As a non-destructive sensing technique, Raman spectroscopy is often combined with regression models for real-time detection of key components in microbial cultivation processes. However, achieving accurate model predictions often requires a large amount of offline measurement data for training, which is both time-consuming and labor-intensive. In order to overcome the limitations of traditional models that rely on large datasets and complex spectral preprocessing, in addition to the difficulty of training models with limited samples, we have explored a genetic algorithm-based semi-supervised convolutional neural network (GA-SCNN). GA-SCNN integrates unsupervised process spectral labeling, feature extraction, regression prediction, and transfer learning. Using only an extremely small number of offline samples of the target protein, this framework can accurately predict protein concentration, which represents a significant challenge for other models. The effectiveness of the framework has been validated in a system of Escherichia coli expressing recombinant ProA5M protein. By utilizing the labeling technique of this framework, the available dataset for glucose, lactate, ammonium ions, and optical density at 600 nm (OD600) has been expanded from 52 samples to 1302 samples. Furthermore, by introducing a small component of offline detection data for recombinant proteins into the OD600 model through transfer learning, a model for target protein detection has been retrained, providing a new direction for the development of associated models. Comparative analysis with traditional algorithms demonstrates that the GA-SCNN framework exhibits good adaptability when there is no complex spectral preprocessing. Cross-validation results confirm the robustness and high accuracy of the framework, with the predicted values of the model highly consistent with the offline measurement results.

Plasmonic-based Raman sensor for ultra-sensitive detection of pharmaceutical waste

Pharmaceutical waste and contaminants pose a significant global concern for water and food safety.

Development and Application of an Automated Raman Sensor for Bioprocess Monitoring: From the Laboratory to an Algae Production Platform.

Microalgae provide valuable bio-components with economic and environmental benefits. The monitoring of microalgal production is mostly performed using different sensors and analytical methods that, although very powerful, are limited to qualified users. This study proposes an automated Raman spectroscopy-based sensor for the online monitoring of microalgal production. For this purpose, an in situ system with a sampling station was made of a light-tight optical chamber connected to a Raman probe. Microalgal cultures were routed to this chamber by pipes connected to pumps and valves controlled and programmed by a computer. The developed approach was evaluated on Parachlorella kessleri under different culture conditions at a laboratory and an industrial algal platform. As a result, more than 4000 Raman spectra were generated and analysed by statistical methods. These spectra reflected the physiological state of the cells and demonstrate the ability of the developed sensor to monitor the physiology of microalgal cells and their intracellular molecules of interest in a complex production environment.

Grating-incoupled waveguide-enhanced Raman sensor.

We report a waveguide-enhanced Raman spectroscopy (WERS) platform with alignment-tolerant under-chip grating input coupling. The demonstration is based on a 100-nm thick planar (slab) tantalum pentoxide (Ta2O5) waveguide and the use of benzyl alcohol (BnOH) and its deuterated form (d7- BnOH) as reference analytes. The use of grating couplers simplifies the WERS system by providing improved translational alignment tolerance, important for disposable chips, as well as contributing to improved Raman conversion efficiency. The use of non-volatile, non-toxic BnOH and d7-BnOH as chemical analytes results in easily observable shifts in the Raman vibration lines between the two forms, making them good candidates for calibrating Raman systems. The design and fabrication of the waveguide and grating couplers are described, and a discussion of further potential improvements in performance is presented.

In Situ and Real-Time Mold Flux Analysis Using a High-Temperature Fiber-Optic Raman Sensor for Steel Manufacturing Applications

Continuous casting in steel production uses specially developed oxyfluoride glasses (mold fluxes) to lubricate a mold and control the solidification of the steel in the mold. The composition of the flux impacts properties, including basicity, viscosity, and crystallization rate, all of which affect the stability of the casting process and the quality of the solidified steel. However, mold fluxes interact with steel during the casting process, resulting in flux chemistry changes that must be considered in the flux design. Currently, the chemical composition of mold flux must be determined by extracting flux samples from the mold during casting and then processing these samples offline to estimate the working chemical composition and, therefore, the expected properties of the flux. Raman spectroscopy offers an alternative method for performing flux analysis with the potential to perform measurements online during the casting process. Raman spectroscopy uniquely identifies specific chemical bonds and symmetries in the glassy flux by revealing peaks that are a fingerprint of the vibration modes of molecules in the flux. The intensities of specific peaks in Raman spectra can be correlated with the chemical composition of the melt and associated properties such as basicity and viscosity. This paper reports on the first use of a portable fiber-optic Raman sensor for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> Raman spectroscopic measurements of molten flux at 1400°C. The work demonstrates the advantages of fiber-optic Raman spectroscopy to document the structure and chemical composition of flux samples at temperatures typically encountered in the mold during continuous caster operation. Experimental results demonstrate that the composition-dependent Raman signal shifts can be detected at caster operating temperatures, and the use of high-temperature Raman analysis for in-line flux monitoring shows significant promise for the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> detection of changes in flux composition and physical properties during casting.

Mathematical modeling and analysis of immiscible metallic based nanofluid flow in a microchannel with non-spherical nanoparticles

The non-spherical shaped nanoparticles have grown in popularity due to their ability to alter the nanofluid thermophysical properties. This nanofluid plays a beneficial role in biomedical imaging, cancer and tumor therapy. In the current study the effect of pressure gradient, transverse magnetic field and electroosmosis on the flow of two immiscible metallic based nanofluids (blood-based Au/Ag Casson nanofluid and water-based Au/Ag Newtonian nanofluid) of different viscosities in separate regions in a microchannel is addressed. The combined impact of electromagnetohydrodynamics (EMHD), modified Darcy’s law, Joule heating and Hall current have been considered into account. The mathematical modeling has been developed with the help of modified Navier Stoke’s equations, and simplified the complex problem using appropriate assumptions and non-dimensional parameters. The complementary function and particular integral concepts have been used to obtain the exact solutions of a non-dimensionalized system of coupled differential equations along with non-dimensionalized conditions. The dimensionless velocity, temperature and concentration profiles of nanofluids have been computed. The influence of numerous relevant fluid transport parameters has been graphically depicted. It is noticed that the magnetic strength declines the nanofluid velocity in both regions. Further, the radiation parameter exhibits an inverse effect on the rate of heat transfer in nanofluids. The heat transfer rate enhanced by 56% and 2% with rising volume fraction [0.01 to 0.02] and zeta potential [0.1 to 0.2], respectively. With increasing strength of magnetic field [2 to 3], thereby 31% increment in shear stress distribution. The results of the current study can be used to enhance the performance of various processes such as surface-enhanced Raman spectroscopy, drug delivery, sensors, and electrochemistry.

Real-time detection and analysis of foodborne pathogens via machine learning based fiber-optic Raman sensor

Food safety is a critical concern worldwide, and in recent years, it has become a growing public concern in the United States due to mass production and multi-channel distribution of high-nutrient and fresh-cut foods that have increased the prevalence and diversity of foodborne pathogens (e.g., Escherichia coli, Salmonella, Listeria, etc.). Traditional methods for the detection of these pathogens rely heavily on low-efficiency techniques, which may expose the public to contaminated foods that have not been tested or identified. Therefore, rapid, simple, sensitive, and inexpensive detection methods are urgently needed for food safety investigations with higher efficiency and accuracy. One of the promising methods for the detection of foodborne pathogens is Raman spectroscopy, which is a molecular-level analytical tool with the advantages of high selectivity and sensitivity and simple operation at a relatively low cost. In this study, a novel fiber-optic-based portable Raman probe was developed and used for real-time detection of a panel of pathogen-specific molecular fingerprint volatile organic compounds (VOCs). Furthermore, machine learning (ML) algorithms were applied to assist in the extraction of molecular information from the raw Raman spectra, resulting in high accuracy prediction of complex VOC mixtures, even at high dilution folds (100x). The developed ML-assisted Raman probe has immense potential for rapid on-site detection of complex chemical mixtures in food safety and beyond. This innovative approach achieves high accuracy in comprehensively sorting mixed chemicals with varying concentrations and provides several advantages over previous studies, including speed, portability, non-contact operation, and precise classification of foodborne pathogen VOCs.

Laser-textured Pd-Au hierarchical superhydrophobic micro-arrays for ultralow SERS detection

Ultra-trace identification of the limited quantity of diluted analyte solutions requires the condensation of target analytes utilizing the superhydrophobic analyte enrichment effect and are extremely desirable for improving the detection sensitivity. Herein, the identification of ultra-trace analyte concentrations employing laser-textured palladium-gold (Pd-Au) hierarchical superhydrophobic micro-arrays as surface-enhanced Raman sensors (SMA-SERS) is reported. The double ns-laser fast scanning strategy produces hierarchically arranged micro-strip and micro-pyramidal arrays over the large surface areas composed of nano-scale inter-particle gaps, enabling the numerous three-dimensional plasmonic hot-spots due to the uniform formation of Au nanoparticles' aggregates/clusters. Under the optimized fabrication parameters, the resultant hybrid SMA-SERS sensors exhibit periodic micro-pyramidal arrays with excellent inherent superhydrophobicity (Contact angle, CA∼171°) and consequently superior SERS performance with uniform (RSD∼10%) signal detectability down to the ∼34.2 fM (3.42×10−14 M) MB molecules utilizing analyte enrichment effect. The accumulation of analytes within the smaller dried spots (Contact diameter, CD∼1.13 mm) results in increased spatial density of analytes and subsequently enables the ultra-low detectability of MGO, DTTCI, PA, AN, adenine, and l-tryp molecules down to ∼3.57 pM, 0.34 fM, 0.31 nM, 0.44 µM, 0.25 nM, and 97.7 nM concentrations, respectively. The multiplexed identification of target (AN and adenine) analytes is explored utilizing serial-deposition of analytes and depict the simultaneous detection of target analytes down to ∼10−7 M (AN) and 10−9 M (adenine) concentrations. The double ns-laser fast scanning strategy provides dual-scale surface roughness in the SMA-SERS substrates and offers ultrahigh sensitivity for the identification of the low amount of analytes utilizing the superhydrophobicity-induced analyte enrichment effect.

Advanced spectral reconstruction (ASR) for setup-independent universal Raman spectroscopy models

Raman spectroscopy is a versatile tool for the non-invasive determination of system or material properties. The desired information about a property or state must always be extracted from the acquired spectra. To that end, a variety of techniques ranging from data-driven machine learning methods to physics-based modeling of spectra are applied. However, most of these techniques suffer from setup dependency, i.e. even minor changes in the experimental setup can lead to major prediction errors when applying previously generated models. Consequently, laborious retraining, recalibration, or even complete rebuilding of a method are required.To weaken this constraint and therefore enable model transfer we propose a novel physics-based method for the advanced spectral reconstruction (ASR) of Raman spectra. It is established on the basic understanding and corresponding modeling of the signal formation process covering all relevant steps from excitation to detection. In contrast to common methods, error-prone preprocessing or other signal manipulation steps are omitted. Instead, experimental influences are explicitly incorporated into the reconstruction model. Hence, this model consists of two independent modules: one modeling the setup-independent fundamental Raman signal and one representing the setup influences. The setup model is determined a priori based on a straightforward setup characterization.To demonstrate the model transfer capabilities of the ASR routine we analyzed multiple liquid samples (water, toluene, and ethanol), polymers (polyethylene and polyamide 6), and a protein (lysozyme) with four different setups. For every pure substance, a parametrized fundamental Raman model could be established from high-quality reference spectra and utilized subsequently in conjunction with the respective setup influence model to reconstruct all experimentally acquired spectra very accurately. Furthermore, an ASR mixture model for the composition determination of binary ethanol/toluene mixtures was set up and calibrated. Again, this model (including the calibration factor) was transferred to evaluate spectra from the same mixtures recorded with different Raman sensors. Without any recalibration (just by implementing the respective instrument model) the evaluation yields an average root mean square error of prediction (RMSEP) of 1.19% highlighting the ability of ASR to transfer or share models for spectral quantification among sensors and/or researchers.

Facile fabrication of porous Ag cubes on Cu substrate for Raman Sensor

• Cu 2 O with cubic structure is fabricated by a facile electrochemical process. • Cu 2 O serves as scarifying template for the synthesis of cubic Ag with porous structure. • The high surface area of porous Ag cubes enables more molecule adsorbed on its surface. • This porous Ag material is a promising material in Raman Sensor. Ag microcubes with a hierarchical porous structure were successfully prepared by a galvanic replacement reaction (GRR) on Cu foil through a subsequently facile two-step process. Firstly, and most importantly, Cu 2 O nano cubes which serves as sacrificial template were synthesized by anodic oxidation of Cu using the dilute hydrochloric acid as an electrolyte for the first time. Compared with traditional thermal synthesis method for the Cu 2 O cubes, the electrochemical method is direct, convenient and cost-effective. Then, the cubic Cu 2 O covered Cu substrate reacts with AgNO 3 and forms porous Ag micro-cubes. The porous Ag with three-dimensional structure is a promising material in Raman Sensor.

Near-unity Raman β-factor of surface-enhanced Raman scattering in a waveguide.

The Raman scattering of light by molecular vibrations is a powerful technique to fingerprint molecules through their internal bonds and symmetries. Since Raman scattering is weak1, methods to enhance, direct and harness it are highly desirable, and this has been achieved using optical cavities2, waveguides3-6 and surface-enhanced Raman scattering (SERS)7-9. Although SERS offers dramatic enhancements2,6,10,11 by localizing light within vanishingly small hot-spots in metallic nanostructures, these tiny interaction volumes are only sensitive to a few molecules, yielding weak signals12. Here we show that SERS from 4-aminothiophenol molecules bonded to a plasmonic gap waveguide is directed into a single mode with >99% efficiency. Although sacrificing a confinement dimension, we find a SERS enhancement of ~103 times across a broad spectral range enabled by the waveguide's larger sensing volume and non-resonant waveguide mode. Remarkably, this waveguide SERS is bright enough to image Raman transport across the waveguides, highlighting the role of nanofocusing13-15 and the Purcell effect16. By analogy to the β-factor from laser physics10,17-20, the near-unity Raman β-factor we observe exposes the SERS technique to alternative routes for controlling Raman scattering. The ability of waveguide SERS to direct Raman scattering is relevant to Raman sensors based on integrated photonics7-9 with applications in gas sensing and biosensing.

Miniaturized 7-in-1 fiber-optic Raman probe.

This Letter reports a miniature 7-in-1 fiber-optic Raman probe that eliminates the inelastic background Raman signal from a long-fused silica fiber. Its foremost purpose is to enhance a method for investigating extraordinarily tiny substances and effectively capturing Raman inelastic backscattered signals using optical fibers. We successfully used our home-built fiber taper device to combine seven multimode fibers into a single fiber taper with a probe diameter of approximately 35 µm. By experimentally comparing the traditional bare fiber-based Raman spectroscopy system with the miniaturized tapered fiber-optic Raman sensor using liquid solutions, the novel probe's capability is demonstrated. We observed that the miniaturized probe effectively removed the Raman background signal originating from the optical fiber and confirmed expected outcomes for a series of common Raman spectra.

Design and Optimization of Tapered Optical Fiber Probes for SERS Utilizing FDTD Method

In this work, we report a strategy of Ag nanoparticle (Ag NP)-coated tapered optical fiber probes by finite difference time domain (FDTD) simulations. Investigation shows that the fiber-tip decorated Ag NPs has excellent electric field enhancement and confinement of light capabilities. Moreover, we demonstrate the effect of key parameters such as tip radius, conical angle, Ag NP size, and gaps between them on the field enhanced utilizing typical excitation wavelengths of 532, 633, and 785 nm. To further improve the electrical field effect, a noble metal substrate is introduced below the tip apex, which exhibits a higher field enhancement generated by tip-substrate coupling. The presence of the Au substrate does not lead to a significant change in the plasma characteristic peak of the probes at 490 nm. This study provides a useful reference for the fabrication of tapered optical fiber with plasmonic nanostructures and the design of robust tapered fiber-optic Raman sensors.

A surface‐enhanced Raman sensor for trace identification and analysis of high‐priority drugs of abuse with portable and handheld Raman devices

AbstractSurface‐enhanced Raman spectroscopy (SERS) is a rapidly emerging technology that offers a fast, extremely sensitive, and quantitative approach to trace chemical detection. Such a technique incorporated into a portable device is attractive to law enforcement and emergency room personnel for rapid accurate on‐site screening of illicit and abused drugs. Toward this, we are developing an analyzer in a hand‐held unit, based on SERS that is integrated with a capillary sensor embedded with activated gold nanoparticles in a porous glass matrix. In this preliminary study, we have used this gold sol–gel capillary to measure aqueous solutions of 14 high‐priority drugs to define sensitivity (lowest measured concentration [LMC] and estimated limit of detection [LOD]), determine concentration dependence and quantification capabilities by constructing calibration curves, examine effects of pH at 3, 7, and 11 on SERS measurements, and perform multicomponent analysis of fentanyl laced drug mixtures and spectral identification on our portable and handheld units. Of particular significance, fentanyl was detected with an LMC of 1 ng/ml and estimated LOD at 0.11 ng/ml, while other representative drugs such as cocaine and phenylcyclidine produced LMCs and LODs at 5.0 and 0.77 ng/ml, and 10.0 and 0.62 ng/ml, respectively. These sub‐nanogram per milliliter detection limits are comparable or lower than previously reported and confirm that this gold sol–gel has the potential to meet the sensitivity requirements for saliva analysis. Most importantly, these sensors can be manufactured easily and cheaply and when integrated with our portable Raman units produce high‐quality spectra and accurate identification within 1.2 s.

Optical Properties of Colloidal Silver Nanowires.

Silver nanowires are used in many applications, ranging from transparent conductive layers to Raman substrates and sensors. Their performance often relies on their unique optical properties that emerge from localized surface plasmon resonances in the ultraviolet. To tailor the nanowire geometry for a specific application, a correct understanding of the relationship between the wire’s structure and its optical properties is therefore necessary. However, while the colloidal synthesis of silver nanowires typically leads to structures with pentagonally twinned geometries, their optical properties are often modeled assuming a cylindrical cross-section. Here we highlight the strengths and limitations of such an approximation by numerically calculating the optical and electrical response of pentagonally twinned silver nanowires and nanowire networks. We find that our accurate modeling is crucial to deduce structural information from experimentally measured extinction spectra of colloidally synthesized nanowire suspensions and to predict the performance of nanowire-based near-field sensors. On the contrary, the cylindrical approximation is fully capable of capturing the optical and electrical performance of nanowire networks used as transparent electrodes. Our results can help assess the quality of nanowire syntheses and guide in the design of optimized silver nanowire-based devices.