Fingerprint Region Research Articles

A laser-based sensor for selective detection of benzene, acetylene, and carbon dioxide in the fingerprint region

A laser-based sensor for selective detection of benzene, acetylene, and carbon dioxide in the fingerprint region

Silver hexacyanoferrate (II) nanocrystals as a new material to improve Raman scattering enhancement during silver surface oxidation

Raman spectroscopy is a powerful analysis technique that shows its full potential when a high amplification of the Raman signal is achieved. In this sense, Surface-Enhanced Raman scattering (SERS) has been the most widely used phenomenon for analysis. SERS provides the amplification of the Raman intensity due to the interaction of molecules with a plasmonic nanostructured surface. The enhancement of the Raman signal can be also obtained during the electrochemical oxidation of a metal electrode; this phenomenon was denoted as Electrochemical-Surface Oxidation Enhanced Raman Scattering (EC-SOERS) and yields a good Raman signal enhancement with high reproducibility. Until now, only chloride and bromide have been employed in EC-SOERS, using a silver electrode to generate silver chloride and silver bromide nanocrystals. In this work, a new EC-SOERS substrate based on the electrogeneration of silver hexacyanoferrate (II) nanocrystals is presented which provides a very sensitive Raman response. The electrogeneration of this new material can be easily followed using spectroelectrochemistry since the characteristic Raman bands of the nanocrystals lie outside of the fingerprint region used for the analysis where the detection of most of the target molecules is performed. Indigo Carmine has been selected as target molecule, obtaining a very good response at nanomolar level under Raman resonance and non-resonance conditions.

Numerical evaluation of spectral coverage and spectral resolution in coherent Raman scattering spectroscopy using a broadband fiber laser source

AbstractWe theoretically investigate the feasibility of using a fiber laser source for high resolution and broadband coherent Raman scattering (CRS) spectroscopy/imaging employing spectral focusing technique. To accomplish this task, we have developed a simulation tool where the laser pulse parameters can be imported directly from any pulse propagation software using split‐step Fourier method to evaluate the spectral resolution and spectral coverage of CRS spectroscopy. Thus, the simulation tool can accommodate optical pulses of arbitrary shape and central wavelength, being also a user‐friendly and versatile open‐source software. We validate the numerical simulation tool by comparing its results with published theoretical and experimental data. We then use the tool to study the feasibility of using an all‐fiber laser source for high resolution CRS spectroscopy in both the C‐H window and the fingerprint region. This tool has a broad scope of applications in multiple fields where CRS spectroscopy is useful such as biomedicine, materials characterization, and biological imaging.

Thermal and molecular characteristics of novel water-soluble polysaccharide from the Pteridium aquilinum and Dryopteris filix-mas

Novel water-soluble complex polysaccharides (CPS) were obtained from Pteridium aquilinum and Dryopteris filix-mas. The yield of CPS was determined to be 4.161%-5.677%. As a result of the physicochemical composition, the CPS contained 12.10–13.65% moisture, 1.983–2.686% protein, 0.123–0.916% lipids and 12.06–21.52% ash. In HPLC analysis, sugar components of the samples (glucose+fructose+saccharose+maltose+lactose) ranged between 35.85–40.98%. Peaks of the fingerprint region specific for polysaccharides were identified during FT-IR analysis. DSC analysis showed that the glassy transition temperature of the DFR sample was higher than in the other three samples. A double cold crystallization peak was detected in all samples. During TGA analysis, the CPS samples exhibited a small amount of mass loss (1.2–1.85%) between 0 and 100 °C, and the DTGmax.p1 region continued up to 215–230 °C. These findings indicate that the four types of polysaccharides have excellent thermal stability (up to 230 °C) and are applicable to high-temperature food processing.

Application of ATR-FTIR Incorporated with Multivariate Data Analysis for Discrimination and Quantification of Urea as an Adulterant in UHT Milk.

Urea is naturally present in milk, yet urea is added intentionally to increase milk's nitrogen content and shelf life. In this study, a total of 50 Ultra heat treatment (UHT) milk samples were spiked with known urea concentrations (0-5 w/v%). Attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy with principal component analysis (PCA), discriminant analysis (DA), and multiple linear regression (MLR) were used for the discrimination and quantification of urea. The PCA was built using 387 variables with higher FL > 0.75 from the first PCA with cumulative variability (90.036%). Subsequently, the DA model was built using the same variables from PCA and demonstrated the good distinction between unadulterated and adulterated milk, with a correct classification rate of 98% for cross-validation. The MLR model used 48 variables with p-value < 0.05 from the DA model and gave R2 values greater than 0.90, with RMSE and MSE below 1 for cross-validation and prediction. The DA and MLR models were then validated externally using a test dataset, which shows 100% correct classification, and the t-test result (p > 0.05) indicated that the MLR could determine the percentage of urea in UHT milk within the permission limit (70 mg/mL). In short, the wavenumbers 1626.63, 1601.98, and 1585.5534 cm-1 are suitable as fingerprint regions for detecting urea in UHT milk.

Mid-Level Data Fusion Combined with the Fingerprint Region for Classification DON Levels Defect of Fusarium Head Blight Wheat.

In this study, a method of mid-level data fusion with the fingerprint region was proposed, which was combined with the characteristic wavelengths that contain fingerprint information in NIR and FT-MIR spectra to detect the DON level in FHB wheat during wheat processing. NIR and FT-MIR raw spectroscopy data on normal wheat and FHB wheat were obtained in the experiment. MSC was used for pretreatment, and characteristic wavelengths were extracted by CARS, MGS and XLW. The variables that can effectively reflect fingerprint information were retained to build the mid-level data fusion matrix. LS-SVM and PLS-DA were applied to investigate the performance of the single spectroscopic model, mid-level data fusion model and mid-level data fusion with fingerprint information model, respectively. The experimental results show that mid-level data fusion with a fingerprint information strategy based on fused NIR and FT-MIR spectra represents an effective method for the classification of DON levels in FHB wheat samples.

Cleavage of disulfide bonds used to reveal manifestation of tertiary structure in the Raman spectra of proteins

Raman spectroscopy is used in the study of the tertiary structure of proteins. The effect of cleavage of disulfide bonds (using DTT and TCEP agents) on the molecular structure and, hence, vibrational spectra is studied. Spectra of two globular proteins (chymotrypsin and bovine serum albumin) are compared with the spectra of the two proteins the structure of which is modified due to cleavage of disulfide bonds. Changes in spectral intervals of 500–550 and 2500–2620 cm-1 indicate disulfide bond breaking. Based on the comparative analysis of the spectral changes in the fingerprint region (in particular, amide I, amide III, tyrosine doublet, bands of disulfide bonds) and low-frequency region, we assume that spectral intervals of 140–470, 500–550, and 800–1000 cm-1 can be used in the study of changes of the tertiary structure.

PhysNet meets CHARMM: A framework for routine machine learning/molecular mechanics simulations.

Full-dimensional potential energy surfaces (PESs) based on machine learning (ML) techniques provide a means for accurate and efficient molecular simulations in the gas and condensed phase for various experimental observables ranging from spectroscopy to reaction dynamics. Here, the MLpot extension with PhysNet as the ML-based model for a PES is introduced into the newly developed pyCHARMM application programming interface. To illustrate the conception, validation, refining, and use of a typical workflow, para-chloro-phenol is considered as an example. The main focus is on how to approach a concrete problem from a practical perspective and applications to spectroscopic observables and the free energy for the -OH torsion in solution are discussed in detail. For the computed IR spectra in the fingerprint region, the computations for para-chloro-phenol in water are in good qualitative agreement with experiment carried out in CCl4. Moreover, relative intensities are largely consistent with experimental findings. The barrier for rotation of the -OH group increases from ∼3.5 kcal/mol in the gas phase to ∼4.1 kcal/mol from simulations in water due to favorable H-bonding interactions of the -OH group with surrounding water molecules.

In-line synthesis of multi-octave phase-stable infrared light.

Parametric downconversion driven by modern, high-power sources of 10-fs-scale near-infrared pulses, in particular intrapulse difference-frequency generation (IPDFG), affords combinations of properties desirable for molecular vibrational spectroscopy in the mid-infrared range: broad spectral coverage, high brilliance, and spatial and temporal coherence. Yet, unifying these in a robust and compact radiation source has remained a key challenge. Here, we address this need by employing IPDFG in a multi-crystal in-line geometry, driven by the 100-W-level, 10.6-fs pulses of a 10.6-MHz-repetition-rate, nonlinearly post-compressed Yb:YAG thin-disk oscillator. Polarization tailoring of the driving pulses using a bichromatic waveplate is followed by a sequence of two crystals, LiIO3 and LiGaS2, resulting in the simultaneous coverage of the 800-cm-1-to-3000-cm-1 spectral range (at -30-dB intensity) with 130 mW of average power. We demonstrate that optical-phase coherence is maintained in this in-line geometry, in theory and experiment, the latter employing ultra-broadband electro-optic sampling. These results pave the way toward coherent spectroscopy schemes like field-resolved and frequency-comb spectroscopy, as well as nonlinear, ultrafast spectroscopy and optical-waveform synthesis across the entire infrared molecular fingerprint region.

Qualitative analysis of broccoli (Brassica oleracea var. italica) glucosinolates: Investigating the use of mid-infrared spectroscopy combined with chemometrics

Glucosinolates are phytochemicals with important health and nutritional benefits. This study reports the use of high-performance liquid chromatography (HPLC) and mid-infrared (MIR) spectroscopy to characterise and differentiate between broccoli varieties and systems of production (organic vs. non-organic) depending on their glucosinolate content and infrared fingerprint. Broccoli samples (n = 53) from seven varieties were analysed using MIR spectroscopy and HPLC. Differences in the MIR spectra of the individual broccoli varieties were observed in the carbohydrate fingerprint region (950–1100 cm-1) and between 1340 and 1615 cm-1 assigned to specific glucosinolates. Principal component analysis (PCA) of the MIR fingerprint spectra enabled the differentiation between samples with relatively high (200–500 mg/100 g DW) and low (< 200 mg/100 g DW) glucobrassicin content. Linear discriminant analysis (LDA) and PCA-LDA were used to classify broccoli varieties according to the system of production (organic vs. non-organic) and variety (common vs. Tenderstem® broccoli). The classification rates indicated that > 70 % of the samples were correctly classified as organic and non-organic, while > 90 % of the samples were correctly classified as common broccoli and Tenderstem®. This study demonstrates that MIR spectroscopy could be used as a potential tool to classify and monitor broccoli samples according to their variety and system of production.

The La+3-, Nd+3-, Bi+3-Doped Ceria as Mixed Conductor Materials for Conventional and Single-Component Solid Oxide Fuel Cells

Clean energy devices are essential in today’s environment to combat climate change and work towards sustainable development. In this paper, the potential materials A2Ce2O7−δ (A = La+3, Nd+3, Bi+3) were analyzed for clean energy devices, specifically for conventional and single-component solid oxide fuel cells (SC-SOFCs). The wet chemical route has been followed for the preparation of samples. X-ray diffraction patterns showed that all three samples exhibited a defected fluorite cubic structure. It also revealed the presence of dopants in the ceria, which was confirmed by the fingerprint region of FTIR. The optical behavior, fuel cell performance and electrochemical behavior were studied by UV–vis, fuel cell testing apparatus and EIS, respectively. The SEM results showed that all samples had irregular polygons. In Raman spectra, the F2g mode corresponding to the space group (Fm3m) confirms the fluorite structure. The Raman spectra showed that A2Ce2O7−δ (A = La+3, Nd+3, Bi+3) have different trends. The conventional fuel cell performance showed that the maximum power density of Bi2Ce2O7 was 0.65 Wcm−2 at 600 °C. The performance of A2Ce2O7−δ (A = La3+, Nd3+, Bi3+) as a single-component fuel cell revealed that Nd2Ce2O7−δ is the best choice with semiconductors conductors ZnO and NCAL. The highest power density (Pmax) of the Nd2Ce2O7/ZnO was 0.58 Wcm−2, while the maximum power output (Pmax) of the Nd2Ce2O7/NCAL was 0.348 Wcm−2 at 650 °C. All the samples showed good agreement with the ZnO as compared to NCAL for SC-SOFCs.

Progress and Perspectives of Mid-Infrared Photoacoustic Spectroscopy for Non-Invasive Glucose Detection.

The prevalence of diabetes is rapidly increasing worldwide and can lead to a range of severe health complications that have the potential to be life-threatening. Patients need to monitor and control blood glucose levels as it has no cure. The development of non-invasive techniques for the measurement of blood glucose based on photoacoustic spectroscopy (PAS) has advanced tremendously in the last couple of years. Among them, PAS in the mid-infrared (MIR) region shows great promise as it shows the distinct fingerprint region for glucose. However, two problems are generally encountered when it is applied to monitor real samples for in vivo measurements in this MIR spectral range: (i) low penetration depth of MIR light into the human skin, and (ii) the effect of other interfering components in blood, which affects the selectivity of the detection system. This review paper systematically describes the basics of PAS in the MIR region, along with recent developments, technical challenges, and data analysis strategies, and proposes improvements for the detection sensitivity of glucose concentration in human bodies. It also highlights the recent trends of incorporating machine learning (ML) to enhance the detection sensitivity of the overall system. With further optimization of the experimental setup and incorporation of ML, this PAS in the MIR spectral region could be a viable solution for the non-invasive measurement of blood glucose in the near future.

Direct Identification and Quantitation of Protein Peptide Powders Based on Multi-Molecular Infrared Spectroscopy and Multivariate Data Fusion.

Given that protein peptide powders (PPPs) from different biological sources were inherited with diverse healthcare functions, which aroused adulteration of PPPs. A high-throughput and rapid methodology, united multi-molecular infrared (MM-IR) spectroscopy with data fusion, could determine the types and component content of PPPs from seven sources as examples. The chemical fingerprints of PPPs were thoroughly interpreted by tri-step infrared (IR) spectroscopy, and the defined spectral fingerprint region of protein peptide, total sugar, and fat was 3600-950 cm-1, which constituted MIR finger-print region. Moreover, the mid-level data fusion model was of great applicability in qualitative analysis, in which the F1-score reached 1 and the total accuracy was 100%, and a robust quantitative model was established with excellent predictive capacity (Rp: 0.9935, RMSEP: 1.288, and RPD: 7.97). MM-IR coordinated data fusion strategies to achieve high-throughput, multi-dimensional analysis of PPPs with better accuracy and robustness which meant a significant potential for the comprehensive analysis of other powders in food as well.

FRDA: Fingerprint Region based Data Augmentation using explainable AI for FTIR based microplastics classification

Marine microplastics (MPs) contamination has become an enormous hazard to aquatic creatures and human life. For MP identification, many Machine learning (ML) based approaches have been proposed using Attenuated Total Reflection Fourier Transform Infrared Spectroscopy (ATR-FTIR). One major challenge for training MP identification models now is the imbalanced and inadequate samples in MP datasets, especially when these conditions are combined with copolymers and mixtures. To improve the ML performance in identifying MPs, data augmentation method is an effective approach. This work utilizes Explainable Artificial Intelligence (XAI) and Gaussian Mixture Models (GMM) to reveal the influence of FTIR spectral regions in identifying each type of MPs. Based on the identified regions, this work proposes a Fingerprint Region based Data Augmentation (FRDA) method to generate new FTIR data to supplement MP datasets. The evaluation results show that FRDA outperforms the existing spectral data augmentation approaches.

Optimization of Affinity Chromatography Based on Sepharose 4B- chitin for Rapid Purification of Urtica dioica Agglutinin.

Today, numerous antimicrobial and anticancer properties have been reported for plant lectins due to their ability to bind to carbohydrates. The Urtica dioica agglutinin (UDA lectin) is a monomeric, small, and low molecular weight glycoprotein. It has attracted the attention of many researchers for identification, treatment, and other clinical purposes. The aim of this study is the optimization of the chitin affinity chromatography based on Sepharose 4B (CNBr-activated Sepharose 4B) for the rapid purification of UDA lectin from Urtica dioica rhizome. The chitin ligands were dissolved in 40% Trichloroacetic acid and attached to Sepharose 4B according to the Amersham-Biosciences instructions. The attachment of the ligand to the Sepharose 4B beads was investigated by Fourier transform infrared (FTIR) spectroscopy. An acidic crude extract of nettle rhizome passes from chromatographic columns in two sizes with dimensions: 24 x 0.51 cm and 8.44 x 0.86 cm. Quantity and quality of purified lectin were calculated by the Bradford microplate method: SDS-PAGE gel electrophoresis and human erythrocyte cell (RBC) hemagglutination, respectively. The analysis of FTIR spectrograms showed that major changes were observed in the fingerprint regions. Besides, due to the dissolution of Sepharose 4B and chitin in the aqueous phase, this difference was not significant in the Imine and Nitrile regions. On the other hand, the comparative results of purification chromatograms showed that increasing the column length causes a smaller half-width and increases the length of the purified peak. Also, it leads to high-quality purified UDA lectin, with a molecular weight of almost 12.5 kDa in gel electrophoresis. Hemagglutination activity on trypsinized red blood cells was displayed, and agglutination of purified UDA lectin started at least at 300 μg.mL-1 concentration. According to our findings, we suggested that dissolving chitin in the polar solvent of Trichloroacetic acid, using Sepharose 4B as the beads of a matrix, and increasing the column length might lead to a decrease in the half-width of the peak. These can increase the purity and concentration of purified UDA lectin, and speed up the purification process. These findings could be used by researchers to accelerate the purification of UDA lectin in other studies, dealing with drug delivery systems, ELISA techniques, and cell growth.

Mid-infrared photoacoustic spectroscopy based on ultrasound detection for blood component analysis.

For the non-invasive measurement of biological tissue, a piezoelectric photoacoustic spectroscopy (PZT-PAS) system that detects a single frequency of ultrasound induced by the irradiation of pulse-modulated mid-infrared laser light was developed. PA spectra of the optical phantom and biological samples were obtained, and the relationship between the PA signal intensity and optical absorbance in the fingerprint region (930-1,200 cm-1) was analyzed to estimate the optical absorbance. The resonance vibration of the induced ultrasound was utilized to further increase the signal strength for biological tissue measurement. Consequently, PA spectrum reflecting the absorption of components in biological tissues was obtained.

GREEN SYNTHESIS, CHARACTERIZATION, PHOTOCATALYTIC ACTIVITY OF ZnO/TiO2 NANOCOMPOSITE FROM Carica papaya LEAVES

This study aims to synthesize a ZnO/TiO2 (ZT) nanocomposite from Carica papaya leaf extract and evaluate its photocatalytic activity. The ZT nanocomposite was prepared using the sol-gel method with ZnO/TiO2 4-gram (ZT4) concentration variations and ZnO/TiO2 6-gram (ZT6). Methylene Blue (MB) was used as a model dye to test its photocatalytic properties. The trend for most activity is shown by ZT4 UV light 40 mg and ZT6 UV light (60 mg), which is 87%. Characterization of ZnO/TiO2 (ZT) nanocomposite using FTIR and XRD The ZT4 nanocomposite had an average crystallite size of approximately 12 nm and crystallinity percentage of 92%, whereas the ZT6 nanocomposite had an average crystallite size of approximately 6 nm and crystallinity percentage of 97%. FTIR resulted in some group C=C stretching alkene, C-H stretching vibrations of an aromatic aldehyde, and O-H stretching of alcohols and Zn-Ti-O in the fingerprint region 393.48 cm-1 to 987.55 cm-1 for ZT4 and in the fingerprint region 401.19 cm-1 to 864.11 cm-1 for ZT6.

Phosphorus adsorption efficiency and characteristic analysis of basic oxygen furnace slag

AbstractA large amount of furnace slag is produced from steelmaking every year. The resultant by‐products will severely damage the natural environment and ecosystems if not treated properly. Businesses worldwide have thus been striving for slag recycling and solving various complex problems. In this study, basic oxygen furnace slag (BOFS) was regarded as an adsorbent to adsorb phosphate in water. In addition to a physical–chemical property analysis of the by‐products, the present study explored the performance of basic oxygen furnace slag in adsorbing phosphorous (P) with different size settings, and observed the surface structure of fused basic oxygen furnace slag.The results revealed that free‐state Ca accounts for the majority in basic oxygen furnace slag content, demonstrating the removal of nearly all phosphorous in water. The results of Fourier‐transform infrared spectroscopy (FT‐IR) on basic oxygen furnace slag with &gt;200 mesh size revealed complex wave crests at the fingerprint region (570–980 cm−1). The result signifies that the basic oxygen furnace slag samples comprise strong Si─O and O─Si─O bonds within silicate minerals. Moreover, basic oxygen furnace slag samples with a particle size &gt;200 mesh contain very high content of lime (CaO) (reaching 49.5%). This property fully demonstrates that basic oxygen furnace slag samples in a small particle size were more active as an aggregate. This study found that the Langmuir adsorption isotherm model (R2 = 0.997) is slightly better than the Freundlich adsorption isotherm model (R2 = 0.984), which shows that the process in which basic oxygen furnace slag adsorbs P is monolayer adsorption, and the adsorption energy is more uniformly distributed among BOFS samples. This study also found that basic oxygen furnace slag samples melted at 1200°C can effectively encapsulate some heavy metal pollutants and form stable glassy slag. The change proved that a fused basic oxygen furnace slag sample could effectively encapsulate heavy metal pollutants and formed glassy‐state slag with high stability. This mechanism would reduce the likelihood of heavy metal leaching when basic oxygen furnace slag serves as a subgrade aggregate, permeable material, or concrete aggregate in the future.

Biochemical characterization and discrimination of Leishmania major parasites and infected macrophages with Raman spectroscopy and chemometrics

Leishmaniasis is classified as one of the neglected tropical disease (NTD), which are caused by a group of parasitic protozoans called Leishmania. The high case load and severity of the disease make Leishmaniasis second only to malaria in terms of both severity and infectivity. However, due to the low economic interest in research and development, it may become a major world-wide health threat. Current diagnostics including serological assessment of infected tissue by either light microscopy, or antibody tests or by the culturing of potential infection via in vitro or in vivo animal inoculation, parasitological tests using samples aspirated from the spleen and bone marrow, Immunological tests such as the Montenegro test, Fluorescence assays, and polymerase chain reaction (PCR) techniques are suffer from several limitations including time and expense. Herein, we first apply Raman microscopy to distinguish the two L. Major parasitic forms namely promastigotes and amastigotes and secondly, distinguish infected from non-infected macrophages using multivariate data analysis including Principal Component Analysis (PCA) and unsupervised hierarchical cluster imaging analysis (UHCA). The maximum variance between infected and uninfected macrophage groups are visible in the lipid region (92.20 %) as compared to the fingerprint region (46.13 %) along PC1. The contributions from nucleic acids can be found at 805 cm−1 (phosphodiester - Z-marker), 767 cm−1 (pyrimidine ring breathing mode), 742 cm−1 (ring breathing mode of DNA/RNA bases), and 568 cm−1 (cytosine/guanine). These amplified nucleic acid signals in infected macrophages indicate the presence of infection compared to the uninfected macrophage group. Similarly, the maximum variance between amastigotes and promastigotes groups are observed in the lipid region (88.45%) as compared to the fingerprint region (28.34 %). Moreover, the UHCA of infected macrophages revealed the accumulation of lipid bodies or droplets inside or close proximity parasitophorous vacuole, which is consistent with the reported literature. Once established macrophages were infected with Leishmania in vitro and the differences between infected and non-infected established with high reproducibility. The reported spectral differences between infected and non-infected macrophages lays the ground work for developing a diagnostic tool for detection of leishmaniasis in a buffy coat preparation and also offers the potential of monitoring the effects of new therapeutics.

Green Synthesis of Selenium Nanoparticles UsingLuffa cylindricaand its Biocompatibility Studies for Potential Biomedical Applications

Selenium nanoparticles (SeNPs), due to their unique properties, have attracted researchers’ attention. Though SeNPs have been used for wide applications, the chemically synthesized one lacks stability due to aggregation, and it releases toxic byproducts. These drawbacks can be overcome by producing SeNPs using natural sources as reducing and capping agents. Luffa cylindrica is an immense source of phytochemical compounds reported for its potential therapeutical value towards cancer, asthma, and sinusitis. In the current study, we have synthesized SeNPs using leaf extract of L. cylindrica and evaluated its biocompatibility and haemocompatibility using peripheral blood mononuclear cells and erythrocytes respectively. The formation of SeNPs was confirmed by a color change from greenish yellow to ruby red during 6 h incubation at 40[Formula: see text]C and further confirmed by the maximum absorbance at 266[Formula: see text]nm and 380[Formula: see text]nm in the UV–Vis spectrum. The fingerprint regions of the Fourier-transform infrared spectroscopy spectrum between 1500[Formula: see text]cm[Formula: see text] and 500[Formula: see text]cm[Formula: see text] revealed the presence of phytoconstituents of L. cylindrica. The particle size analysis showed a size range of 100[Formula: see text]nm and zeta potential of −13.6 mV. Scanning electron micrograph showed flower-shaped surface morphology with a size range of 100[Formula: see text]nm. The erythrocytes treated with higher concentrations of LC-SeNPs showed less than 5% lysis compared to the positive control. Similarly, in the apoptosis assay, 80.45% of cells remained viable after being treated with LC-SeNPs, which is comparable with that of untreated control. Since the synthesized SeNPs possess biocompatibility and are less cytotoxic, they could be used in cardiac tissue engineering applications. However, further in vitro and in vivo studies are required to confirm its role in cardiac tissue engineering.