Development of a low-cost graphene-based impedance biosensor

PhD ThesisThe current applicability and accuracy of point-of-care devices is limited, with the need of future technologies to simultaneously target multiple analytes in complex human samples. Graphene’s discovery has provided a valuable opportunity towards the development of high performance biosensors. The quality and surface properties of graphene devices are critical for biosensing applications with a preferred low contact resistance interface between metal and graphene. However, each graphene production method currently results in inconsistent properties, quality and defects thus limiting its application towards mass production. Also, post-production processing, patterning and conventional lithography-based contact deposition negatively impact graphene properties due to chemical contamination. The work of this thesis focuses on the development of fully-functional, label-free graphene-based biosensors and a proof-of-concept was established for the detection of prostate specific antigen (PSA) in aqueous solution using graphene platforms. Extensive work was carried out to characterize different graphene family nanomaterials in order to understand their potential for biosensing applications. Two graphene materials, obtained via a laser reduction process, were selected for further investigations: reduced graphene oxide (rGO) and laser induced graphene from polyimide (LIG). Electrically conductive, porous and chemically active to an extent, these materials offer the advantage of simultaneous production and patterning as capacitive biosensing structures, i.e. interdigitated electrode arrays (IDE). Aiming to enhance the sensitivity of these biosensors, a novel, radio-frequency (RF) detection method was investigated and compared with conventional electrochemical impedance spectroscopy (EIS) on a well-known biocompatible material: gold (standard). It was shown that the RF detection methods require careful design and testing setup, with conventional EIS performing better in the given conditions. The method was further used on rGO and LIG IDE devices for the electrochemical impedance detection of PSA to assess the feasibility of the graphene based materials as biosensors. The graphene-based materials were successfully functionalized via the available carboxylic groups, using the EDC-NHS chemistry. Despite the difficulty of producing reproducible graphene-based electrodes, highly required for biosensor development, extensive testing was carried out to understand their feasibility. The calibration curves obtained via successive PSA addition showed a moderate-to-high ii sensitivity of both rGO and LIG IDE. However, further adsorption and drift testing underlined some major limitations in the case of LIG, due to its complex morphology and large porosity. To enable low contact resistance to these biosensors, the electroless nickel coating process is shown to be compatible with various graphene-based materials. This was demonstrated by tuning the chemical nickel bath and method conditions for pristine graphene and rGO for nickel contacts deposition

Rapid Prototyping of a Low-cost Graphene-based Impedimetric Biosensor

This paper presents a preliminary investigation towards rapid prototyping of a low-cost biosensor based on reduced graphene oxide (rGO). The devices are fabricated via a laser scribing process and their functionality is demonstrated by their functionalization and subsequent immobilization of 7% bovine serum albumin (BSA). Non-faradaic electrochemical impedance spectroscopy (EIS) indicated a 33-42% decrease in impedance upon immobilization. An electroless nickel deposition process is demonstrated to enable electrical contacts to the device, with optimized plating conditions (pH, temperature) leading to a rGO-nickel contact resistance of 19 Ω/mm2

Electroless Nickel Deposition:An Alternative for Graphene Contacting

We report the first investigation into the potential of electroless nickel deposition to form ohmic contacts on single layer graphene. To minimize the contact resistance on graphene, a statistical model was used to improve metal purity, surface roughness, and coverage of the deposited film by controlling the nickel bath parameters (pH and temperature). The metalized graphene layers were patterned using photolithography and contacts deposited at temperatures as low as 60 °C. The contact resistance was 215 ± 23 ω over a contact area of 200 μm × 200 μm, which improved upon rapid annealing to 107 ± 9 ω. This method shows promise toward low-cost and large-scale graphene integration into functional devices such as flexible sensors and printed electronics

Chemically specific identification of carbon in XPS imaging using Multivariate Auger Feature Imaging (MAFI)

Until now, a difficult prospect in XPS imaging has been the identification of similar chemical states of carbon. With the advent of novel nano-carbons such as nanotubes and graphene, the ability to easily and unambiguously identify materials of varying sp2/sp3 nature in XPS spectra and images is becoming increasingly important. We present herein methods for the identification of such species in XPS images by shifting focus from the traditionally analysed C1s region to the X-ray induced carbon Auger feature. By extracting the D-Parameter from XPS data, we have generated what we refer to as "D-Parameter Images", that clearly identify regions of different carbon hybridisation in an image of a graphite flake mounted on carbon tape, and areas of reduced graphene oxide (GO) in a laser-scribed GO film. This method is then enhanced by multivariate analysis, a technique we call "Multivariate Auger Feature Imaging", where the distinction between varying sp2 carbon content on a surface is improved

Predictive Performance of Machine Learning-Based Methods for the Prediction of Preeclampsia—A Prospective Study

(1) Background: Preeclampsia (PE) prediction in the first trimester of pregnancy is a challenge for clinicians. The aim of this study was to evaluate and compare the predictive performances of machine learning-based models for the prediction of preeclampsia and its subtypes. (2) Methods: This prospective case-control study evaluated pregnancies that occurred in women who attended a tertiary maternity hospital in Romania between November 2019 and September 2022. The patients’ clinical and paraclinical characteristics were evaluated in the first trimester and were included in four machine learning-based models: decision tree (DT), naïve Bayes (NB), support vector machine (SVM), and random forest (RF), and their predictive performance was assessed. (3) Results: Early-onset PE was best predicted by DT (accuracy: 94.1%) and SVM (accuracy: 91.2%) models, while NB (accuracy: 98.6%) and RF (accuracy: 92.8%) models had the highest performance when used to predict all types of PE. The predictive performance of these models was modest for moderate and severe types of PE, with accuracies ranging from 70.6% and 82.4%. (4) Conclusions: The machine learning-based models could be useful tools for EO-PE prediction and could differentiate patients who will develop PE as early as the first trimester of pregnancy

Dual Stem Cell Therapy Improves the Myocardial Recovery Post-Infarction through Reciprocal Modulation of Cell Functions

Mesenchymal stromal cells (MSC) are promising candidates for regenerative therapy of the infarcted heart. However, poor cell retention within the transplantation site limits their potential. We hypothesized that MSC benefits could be enhanced through a dual-cell approach using jointly endothelial colony forming cells (ECFC) and MSC. To assess this, we comparatively evaluated the effects of the therapy with MSC and ECFC versus MSC-only in a mouse model of myocardial infarction. Heart function was assessed by echocardiography, and the molecular crosstalk between MSC and ECFC was evaluated in vitro through direct or indirect co-culture systems. We found that dual-cell therapy improved cardiac function in terms of ejection fraction and stroke volume. In vitro experiments showed that ECFC augmented MSC effector properties by increasing Connexin 43 and Integrin alpha-5 and the secretion of healing-associated molecules. Moreover, MSC prompted the organization of ECFC into vascular networks. This indicated a reciprocal modulation in the functionality of MSC and ECFC. In conclusion, the crosstalk between MSC and ECFC augments the therapeutic properties of MSC and enhances the angiogenic properties of ECFC. Our data consolidate the dual-cell therapy as a step forward for the development of effective treatments for patients affected by myocardial infarction

Fully controllable silicon nanowire fabricated using optical lithography and orientation dependent oxidation

Silicon nanowires (SiNWs) exhibit unique electrical, thermal, and optical properties compared to bulk silicon which make them suitable for various device applications. To realize nanowires in real applications, large-scale and low-cost fabrication method is required. Here, we demonstrate a simple, low-cost fabrication process to produce silicon nanowires (SiNWs) with full controllability of size and length. The nanowires are fabricated using optical lithography and orientation dependent oxidation. Highly uniform single crystalline nanowires with thicknesses down to 10 nm, lengths up to 3 cm and aspect ratios up to approximately 300,000 are formed with high yield. The technology is further simplified to fabricate more complex structure such as metal-oxide-semiconductor field-effect-transistors (MOSFETs) by means of the selective etching of silicon without the need for extra steps. This method is distinct from other top-down techniques, where the formation of nanowires at low-cost, using simple processing steps, with high controllability and reproducibility is major challenge. This controllable and CMOS-compatible technology can offer a practical route to fabricate nanostructures with tuneable properties that can be the key for many device applications including nanoelectronics, thermoelectric and biosensing

Repair of the Orbital Wall Fractures in Rabbit Animal Model Using Nanostructured Hydroxyapatite-Based Implant

Cellular uptake and cytotoxicity of nanostructured hydroxyapatite (nanoHAp) are dependent on its physical parameters. Therefore, an understanding of both surface chemistry and morphology of nanoHAp is needed in order to be able to anticipate its in vivo behavior. The aim of this paper is to characterize an engineered nanoHAp in terms of physico-chemical properties, biocompatibility, and its capability to reconstitute the orbital wall fractures in rabbits. NanoHAp was synthesized using a high pressure hydrothermal method and characterized by physico-chemical, structural, morphological, and optical techniques. X-ray diffraction revealed HAp crystallites of 21 nm, while Scanning Electron Microscopy (SEM) images showed spherical shapes of HAp powder. Mean particle size of HAp measured by DLS technique was 146.3 nm. Biocompatibility was estimated by the effect of HAp powder on the adhesion and proliferation of mesenchymal stem cells (MSC) in culture. The results showed that cell proliferation on powder-coated slides was between 73.4% and 98.3% of control cells (cells grown in normal culture conditions). Computed tomography analysis of the preformed nanoHAp implanted in orbital wall fractures, performed at one and two months postoperative, demonstrated the integration of the implants in the bones. In conclusion, our engineered nanoHAp is stable, biocompatible, and may be safely considered for reconstruction of orbital wall fractures

Ganglioneuroma of the Bladder in Association with Neurofibromatosis Type 1

Neurofibromatosis type 1 (NF1) is a genetic disease, with autosomal dominant transmission, related to pathogenic variant of the tumor suppressor gene NF1 (17q11.2), predisposing affected subjects to a variety of benign (neurofibromas and plexiform neurofibromas) and malignant tumors. The lack of the NF1-neurofibromin gene product can cause uncontrolled cell proliferation in the central or peripheral nervous system and multisystemic involvement, and so the disease includes a heterogeneous group of clinical manifestations. Ganglioneuromas are benign tumors developing from the neural crest cells of the autonomic nervous system, considered to be part of neuroblastic tumors. Bladder localization is extremely rare in adults, and only three such cases were reported in children so far. The aim of our study, in addition to a brief review of the literature of these pathologies, is to bring to your attention the case of a sixteen year old patient with a very rare association of NF1 and bladder ganglioneuroma, who presented at the hospital with gross hematuria. Since bladder ganglioneuroma is a rare pathological condition, the differential diagnosis is difficult and imaging investigations and pathological investigations are the ones that elucidate this disease. The clinical approach of the medical multidisciplinary team involved should help the patient in managing her medical and surgical situation

Machine Learning Approaches for the Prediction of Hepatitis B and C Seropositivity

(1) Background: The identification of patients at risk for hepatitis B and C viral infection is a challenge for the clinicians and public health specialists. The aim of this study was to evaluate and compare the predictive performances of four machine learning-based models for the prediction of HBV and HCV status. (2) Methods: This prospective cohort screening study evaluated adults from the North-Eastern and South-Eastern regions of Romania between January 2022 and November 2022 who underwent viral hepatitis screening in their family physician’s offices. The patients’ clinical characteristics were extracted from a structured survey and were included in four machine learning-based models: support vector machine (SVM), random forest (RF), naïve Bayes (NB), and K nearest neighbors (KNN), and their predictive performance was assessed. (3) Results: All evaluated models performed better when used to predict HCV status. The highest predictive performance was achieved by KNN algorithm (accuracy: 98.1%), followed by SVM and RF with equal accuracies (97.6%) and NB (95.7%). The predictive performance of these models was modest for HBV status, with accuracies ranging from 78.2% to 97.6%. (4) Conclusions: The machine learning-based models could be useful tools for HCV infection prediction and for the risk stratification process of adult patients who undergo a viral hepatitis screening program