Browsing by Author "Sardar, Rajesh"

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    Achieving biosensing at attomolar concentrations of cardiac troponin T in human biofluids by developing a label-free nanoplasmonic analytical assay
    (RSC, 2017) Liyanage, Thaksila; Sangha, Andeep; Sardar, Rajesh; Chemistry and Chemical Biology, School of Science
    Acute myocardial infarction (heart attack) is the fifth leading cause of death in the United States (Dariush et al., Circulation, 2015, 131, e29–e322). This highlights the need for early, rapid, and sensitive detection of its occurrence and severity through assaying cardiac biomarkers in human fluids. Herein we report chip-based fabrication of the first label-free, nanoplasmonic biosensor to assay cardiac troponin T (cTnT) in human biofluids (plasma, serum, and urine) with high specificity. The sensing mechanism is based on the adsorption model that measures the localized surface plasmon resonance (LSPR) wavelength shift of anti-cTnT functionalized gold triangular nanoprisms (Au TNPs) induced by a change of their local dielectric environment upon binding of cTnT. We demonstrate that controlled manipulation of the sensing volume and decay length of Au TNPs together with an appropriate surface functionalization and immobilization of anti-cTnT onto TNPs allows us to achieve a limit of detection (LOD) of our cTnT assay at attomolar concentration (∼15 aM) in human plasma. This LOD is at least 50-fold more sensitive than that of other label-free techniques. Furthermore, we demonstrate excellent sensitivity of our sensors in human serum and urine. Importantly, our chip-based fabrication strategy is extremely reproducible. We believe our powerful analytical tool for detection of cTnT directly in human biofluids using this highly reproducible, label-free LSPR sensor will have great potential for early diagnosis of heart attack and thus increase patients’ survival rate.
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    Advances in Solid Phase Microextraction for the Analysis of Volatile Compounds in Explosives, Tire Treatments, and Entomological Specimens
    (2016-05) Kranz, William D.; Goodpaster, John V.; Manicke, Nick; Sardar, Rajesh; Picard, Christine Johanna; Long, Eric C.
    Solid phase micro-extraction is a powerful and versatile technique, well-suited to the analysis of numerous samples of forensic interest. The exceptional sensitivity of the SPME platform, combined with its adaptability to traditional GC-MS systems and its ability to extract samples with minimal work-up, make it appropriate to applications in forensic laboratories. In a series of research projects, solid phase micro-extraction was employed for the analysis of explosives, commercial tire treatments, and entomological specimens. In the first project, the volatile organic compounds emanating from two brands of pseudo-explosive training aids for use in detector dog imprinting were determined by SPME-GC-MS, and the efficacy of these training materials was tested in live canine trials. In the second project, the headspace above various plasticizers was analyzed comparative to that of Composition C-4 in order to draw conclusions about the odor compound, 2- ethyl-1-hexnaol, with an eye toward the design of future training aids. In the third, automobile tires which had participated in professional race events were analyzed for the presence of illicit tire treatments, and in the fourth, a novel SPME-GC-MS method was developed for the analysis of blowfly (Diptera) liquid extracts. In the fifth and final project, the new method was put to the task of performing a chemotaxonomic analysis on pupa specimens, seeking to chemically characterize them according to their age, generation, and species.
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    Amplification-Free, High-Throughput Nanoplasmonic Quantification of Circulating MicroRNAs in Unprocessed Plasma Microsamples for Earlier Pancreatic Cancer Detection
    (ACS, 2023-03) Masterson, Adrianna N.; Chowdhury, Nayela N.; Yang, Yue; Yip-Schneider, Michele T.; Hati, Sumon; Gupta, Prashant; Cao, Sha; Wu, Huangbing; Schmidt, C. Max; Fishel, Melissa L.; Sardar, Rajesh; Chemistry, School of Science
    Pancreatic ductal adenocarcinoma (PDAC) is a deadly malignancy that is often detected at an advanced stage. Earlier diagnosis of PDAC is key to reducing mortality. Circulating biomarkers such as microRNAs are gaining interest, but existing technologies require large sample volumes, amplification steps, extensive biofluid processing, lack sensitivity, and are low-throughput. Here, we present an advanced nanoplasmonic sensor for the highly sensitive, amplification-free detection and quantification of microRNAs (microRNA-10b, microRNA-let7a) from unprocessed plasma microsamples. The sensor construct utilizes uniquely designed −ssDNA receptors attached to gold triangular nanoprisms, which display unique localized surface plasmon resonance (LSPR) properties, in a multiwell plate format. The formation of −ssDNA/microRNA duplex controls the nanostructure–biomolecule interfacial electronic interactions to promote the charge transfer/exciton delocalization processes and enhance the LSPR responses to achieve attomolar (10–18 M) limit of detection (LOD) in human plasma. This improve LOD allows the fabrication of a high-throughput assay in a 384-well plate format. The performance of nanoplasmonic sensors for microRNA detection was further assessed by comparing with the qRT-PCR assay of 15 PDAC patient plasma samples that shows a positive correlation between these two assays with the Pearson correlation coefficient value >0.86. Evaluation of >170 clinical samples reveals that oncogenic microRNA-10b and tumor suppressor microRNA-let7a levels can individually differentiate PDAC from chronic pancreatitis and normal controls with >94% sensitivity and >94% specificity at a 95% confidence interval (CI). Furthermore, combining both oncogenic and tumor suppressor microRNA levels significantly improves differentiation of PDAC stages I and II versus III and IV with >91% and 87% sensitivity and specificity, respectively, in comparison to the sensitivity and specificity values for individual microRNAs. Moreover, we show that the level of microRNAs varies substantially in pre- and post-surgery PDAC patients (n = 75). Taken together, this ultrasensitive nanoplasmonic sensor with excellent sensitivity and specificity is capable of assaying multiple biomarkers simultaneously and may facilitate early detection of PDAC to improve patient care.
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    Bottom-Up Fabrication of Plasmonic Nanoantenna-Based High-throughput Multiplexing Biosensors for Ultrasensitive Detection of microRNAs Directly from Cancer Patients’ Plasma
    (ACS, 2020) Masterson, Adrianna N.; Liyanage, Thakshila; Kaimakliotis, Hristos; Derami, Hamed Gholami; Deiss, Frédérique; Sardar, Rajesh; Chemistry and Chemical Biology, School of Science
    There is an unmet need in clinical point-of-care (POC) cancer diagnostics for early state disease detection, which would greatly increase patient survival rates. Currently available analytical techniques for early stage cancer diagnosis do not meet the requirements for POC of a clinical setting. They are unable to provide the high demand of multiplexing, high-throughput, and ultrasensitive detection of biomarkers directly from low volume patient samples (“liquid biopsy”). To overcome these current technological bottle-necks, herein we present, for the first time, a bottom-up fabrication strategy to develop plasmonic nanoantenna-based sensors that utilize the unique localized surface plasmon resonance (LSPR) properties of chemically synthesized gold nanostructures, gold triangular nanoprisms (Au TNPs), gold nanorods (Au NRs), and gold spherical nanoparticles (Au SNPs). Our Au TNPs, NRs, and SNPs display refractive index unit (RIU) sensitivities of 318, 225, and 135 nm/RIU respectively. Based on the RIU results, we developed plasmonic nanoantenna-based multiplexing and high-throughput biosensors for the ultrasensitive assay of microRNAs. MicroRNAs are directly linked with cancer development, progression, and metastasis, thus they hold promise as next generation biomarkers for cancer diagnosis and prognosis. The developed biosensors are capable of assaying five different types of microRNAs at an attomolar detection limit. These sets of microRNAs include both oncogenic and tumor suppressor microRNAs. To demonstrate the efficiency as a POC cancer diagnostic tool, we analyzed the plasma of 20-bladder cancer patients without any sample processing steps. Importantly, our liquid biopsy-based biosensing approach is capable of differentiating healthy from early (“non-metastatic”) and late (“metastatic”) stage cancer with a p value <0.0001. Further, receiver operating characteristic analysis shows that our biosensing approach is highly specific, with an area under the curve of 1.0. Additionally, our plasmonic nanoantenna-based biosensors are regenerative, allowing multiple measurements using the same biosensors, which is essential in low- and middle-income countries. Taken together, our multiplexing and high-throughput biosensors have the unmatched potential to advance POC diagnostics and meet global needs for early stage detection of cancer and other diseases (e.g., infectious, autoimmune, and neurogenerative diseases).
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    Chemometric Comparison Of GC-MS And GC-VUV For The Trace Analysis Of Methamphetamine
    (2024) Lyle, Grant; Goodpaster, John; Sardar, Rajesh; Manicke, Nicholas
    Chemometrics, the application of mathematical or statistical algorithms to make inferences on the state of a chemical system from physical measurements of it, is a powerful tool that can be used to re-read what previously was observed as ‘noise’ in analytical measurements. Instruments such as spectrophotometers can take thousands of measurements over a predefined interval, but the spectra are only of great use when reference libraries exist, or if large trends occur that allow for visual matching, such as with a particular functional group. Application of statistical techniques to these data, such as principal component analysis (PCA) and linear discriminant analysis (LDA), can help to spot underlying variances, and differentiate between similar spectra by using linear combinations of these variables for classification. Methamphetamine (MA) is a member of the phenethylamines, a group of compounds that act as central nervous system stimulants, which are highly addictive and often the subject of law enforcement efforts at the local and federal level. Use of derivatization agents in analysis of seized narcotics is common practice, as it increases volatility/thermal stability of analytes, and improves peak shape for chromatographic resolution. In this analysis, we looked to investigate the difference in instrumental response for MA in its native form, as well as derivatized with two common agents, acetic anhydride and trifluoroacetic anhydride. These three forms were analyzed both on a gas chromatograph- mass spectrometer (GC-MS) and a gas chromatograph- vacuum ultraviolet spectrometer (GC-VUV). The raw GC-MS and GC-VUV data were separately normalized, and the dimensionality of the data was reduced through PCA, which uses orthogonal linear transformations of the data to capture most of the variance between datasets while simultaneously reducing the dimensionality for further analysis. Linear discriminant analysis was utilized to look at the principal components from PCA, and a classification model was built for use in discriminating between forms of methamphetamine from compressed datasets.
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    Chemometrics applied to the discrimination of synthetic fibers by microspectrophotometry
    (2014-01-03) Reichard, Eric Jonathan; Goodpaster, John V. (John Vincent); Minto, Robert; Sardar, Rajesh; Siegel, Jay A.; Picard, Christine
    Microspectrophotometry is a quick, accurate, and reproducible method to compare colored fibers for forensic purposes. The use of chemometric techniques applied to spectroscopic data can provide valuable discriminatory information especially when looking at a complex dataset. Differentiating a group of samples by employing chemometric analysis increases the evidential value of fiber comparisons by decreasing the probability of false association. The aims of this research were to (1) evaluate the chemometric procedure on a data set consisting of blue acrylic fibers and (2) accurately discriminate between yellow polyester fibers with the same dye composition but different dye loadings along with introducing a multivariate calibration approach to determine the dye concentration of fibers. In the first study, background subtracted and normalized visible spectra from eleven blue acrylic exemplars dyed with varying compositions of dyes were discriminated from one another using agglomerative hierarchical clustering (AHC), principal component analysis (PCA), and discriminant analysis (DA). AHC and PCA results agreed showing similar spectra clustering close to one another. DA analysis indicated a total classification accuracy of approximately 93% with only two of the eleven exemplars confused with one another. This was expected because two exemplars consisted of the same dye compositions. An external validation of the data set was performed and showed consistent results, which validated the model produced from the training set. In the second study, background subtracted and normalized visible spectra from ten yellow polyester exemplars dyed with different concentrations of the same dye ranging from 0.1-3.5% (w/w), were analyzed by the same techniques. Three classes of fibers with a classification accuracy of approximately 96% were found representing low, medium, and high dye loadings. Exemplars with similar dye loadings were able to be readily discriminated in some cases based on a classification accuracy of 90% or higher and a receiver operating characteristic area under the curve score of 0.9 or greater. Calibration curves based upon a proximity matrix of dye loadings between 0.1-0.75% (w/w) were developed that provided better accuracy and precision to that of a traditional approach.
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    Colloidal Synthesis of Single-Layer Quasi-Ruddlesden–Popper Phase Bismuth-Based Two-Dimensional Perovskite Nanosheets with Controllable Optoelectronic Properties
    (ACS, 2021-07) Lee, Jacob T.; Seifert, Soenke; Sardar, Rajesh; Chemistry, School of Science
    Single- and few-layered two-dimensional (2D) nanomaterials have attracted intense research interest in the last two decades due to their unique electronic and optoelectronic properties leading to various potential applications. Herein, we report the colloidal synthesis of Bi-based 2D perovskite nanosheets (PEG6-NH3+)nCs3–nBi2X9, where X = Cl, Br, and I, through careful design of reaction conditions and selection of poly(ethylene glycol) (PEG6) surface passivating ligands. The 2D nanosheets are ∼5 nm in thickness with micron-sized lateral dimensions and display composition-dependent band gap and work function modulation. Small-angle X-ray scattering analysis substantiates that the individual inorganic crystal layer, Cs3–nBi2X9, is separated by the spacer, PEG6 ligand. Additionally, we determined that PEG6-NH2 is an essential passivating ligand and spacer for the formation of Bi-based 2D nanosheets. Most importantly, controlled crystallization of the colloidal dispersion of nanosheets results in the formation of superlattice microstructures of the quasi-Ruddlesden–Popper phase. These microstructures can be exfoliated to ultrathin nanosheets by overcoming the van der Waals interaction between the organic passivating layers. The controlled synthesis of lead-free 2D perovskite nanosheets presented here can expand their utility to photocatalytic and optoelectronic applications with reduced toxicity.
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    Combining Semiempirical QM Methods with Atom Dipole Interaction Model for Accurate and Efficient Polarizability Calculations
    (2022-12) Young, Ryan; Pu, Jingzhi; Long, Eric C.; Naumann, Christoph; Sardar, Rajesh
    Molecular polarizability plays a significant role in chemistry, biology, and medicine. Classical prediction of polarizability often relies on atomic-type specific polarizability optimized for training set molecules, which limits the calculations to systems of similar chemical environment. Although ab initio (AI) quantum mechanical (QM) methods are more transferable in predicting molecular polarizability, their high computational costs especially when used with large basis sets for obtaining quantitatively reliable results make them less practical. To obtain accurate QM polarizability in an efficient manner, we have developed a dual-level approach, where the polarizability (α) obtained from the efficient semiempirical QM (SE) method is corrected using a set of element-base atomic polarizabilities derived from the atomic dipole interaction model (ADIM) to reproduce the density functional theory (DFT) results. We have optimized the atomic polarizability correction parameters for CHON-containing systems using a small training set of molecules and tested the resulting SE-ADIM model on the neutral drug-like molecules in the QM7B database. SE-ADIM corrected AM1 showed substantial improvement with its relative percent error (RPE) compared to B3LYP reduced from 33.81% to 3.35%. To further test its robustness for larger molecules in broader chemical bonding situations, we applied this method to a collection of drug molecules from the e-Drug3D database. For the 1004 molecules tested, our SE-ADIM model, which only contains four empirical parameters, greatly reduces the RPE in AM1 polarizability relative to B3LYP from 26.8% to 2.9%. Error decomposition shows consistent improvements across molecules with diverse bond saturations, molecular sizes, and charge states. In addition, we have applied AlphaML, a promising machine learning (ML) technique for predicting molecular polarizability, to the e-Drug3D dataset to compare its performance with our SE-ADIM correction of AM1. We found SE-ADIM performs competitively with AlphaML bolstering our confidence in the value of our method. Errors distinct to AlphaML were also discovered. We found four molecules for which AlphaML predicts negative molecular polarizabilities, all of which were peroxides. In contrast, SE-ADIM has no such issue with these molecules or this chemical type. Finally, to improve performance of SE-ADIM when correcting AM1 molecular polarizability calculations for charged molecules, we introduce a charge dependent polarizability (CDP) enabled SE-ADIM. Training the CDP enabled SE-ADIM with a single additional parameter, B, we were able to reduce error in AM1 molecular polarizability calculations of charged molecules relative to B3LYP from 29.57% to 5.16%. By contrast, SE-ADIM without CDP corrected AM1 relative to B3LYP had an RPE of 8.56%. The most benefit of CDP was evident within negatively charged molecules where AM1 error relative to B3LYP fell from 32.20% to 3.77% while SE-ADIM without CDP enabled error for these same negative molecules was 10.06%.
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    A Computational Study of the Mechanism for F1-ATPase Inhibition by the Epsilon Subunit
    (2013) Thomson, Karen J.; Pu, Jingzhi; Ge, Haibo; Sardar, Rajesh; Long, Eric C. (Eric Charles)
    The multi-protein complex of F0F1 ATP synthase has been of great interest in the fields of microbiology and biochemistry, due to the ubiquitous use of ATP as a biological energy source. Efforts to better understand this complex have been made through structural determination of segments based on NMR and crystallographic data. Some experiments have provided useful data, while others have brought up more questions, especially when structures and functions are compared between bacteria and species with chloroplasts or mitochondria. The epsilon subunit is thought to play a signi cant role in the regulation of ATP synthesis and hydrolysis, yet the exact pathway is unknown due to the experimental difficulty in obtaining data along the transition pathway. Given starting and end point protein crystal structures, the transition pathway of the epsilon subunit was examined through computer simulation.The purpose of this investigation is to determine the likelihood of one such proposed mechanism for the involvement of the epsilon subunit in ATP regulation in bacterial species such as E. coli.
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    Covalent Surface Modification of Ti3C2Tx MXene with Chemically Active Polymeric Ligands Producing Highly Conductive and Ordered Microstructure Films
    (American Chemical Society (ACS), 2021-11-17) Lee, Jacob T.; Wyatt, Brian C.; Davis, Gregory A., Jr.; Masterson, Adrianna N.; Pagan, Amber L.; Shah, Archit; Anasori, Babak; Sardar, Rajesh; Chemistry, School of Science
    As interest continues to grow in Ti3C2Tx and other related MXenes, advancement in methods of manipulation of their surface functional groups beyond synthesis-based surface terminations (Tx: −F, −OH, and ═O) can provide mechanisms to enhance solution processability as well as produce improved solid-state device architectures and coatings. Here, we report a chemically important surface modification approach in which “solvent-like” polymers, polyethylene glycol carboxylic acid (PEG6-COOH), are covalently attached onto MXenes via esterification chemistry. Surface modification of Ti3C2Tx with PEG6-COOH with large ligand loading (up to 14% by mass) greatly enhances dispersibility in a wide range of nonpolar organic solvents (e.g., 2.88 mg/mL in chloroform) without oxidation of Ti3C2Tx two-dimensional flakes or changes in the structure ordering. Furthermore, cooperative interactions between polymer chains improve the nanoscale assembly of uniform microstructures of stacked MXene-PEG6 flakes into ordered thin films with excellent electrical conductivity (∼16,200 S·cm–1). Most importantly, our covalent surface modification approach with ω-functionalized PEG6 ligands (ω-PEG6-COOH, where ω: −NH2, −N3, −CH═CH2) allows for control over the degree of functionalization (incorporation of valency) of MXene. We believe that installing valency onto MXenes through short, ion conducting PEG ligands without compromising MXenes’ features such as solution processability, structural stability, and electrical conductivity further enhance MXenes surface chemistry tunability and performance and widens their applications.