Detection of particles, bacteria and viruses using consumer optoelectronic components

The focus of this thesis is on the design, development and validation of two novel photonic sensors for the detection and characterisation of industrial and biological samples. The first one is a PSA in a collimated beam configuration using an innovative angular spatial filter, and a consumer electronic camera similar to that used in a smartphone. The small form factor angular spatial filter allows for the collection of diffused light from particles up to predefined discrete angles. By using angularly resolved scattering images acquired by the camera, a machine learning (ML) algorithm predicts the volume median diameter of the particles. Our system has achieved a mean absolute percentage error of only 0.72% for spherical particles in solution with sizes greater than 10 µm and at concentrations up to 40 mg mL-1. Compared to traditional laser diffraction systems, the proposed PSA is an order of magnitude smaller in size, weight and cost, and offers a promising approach to online industrial process monitoring. As light scattering is influenced by factors other than particle size, including shape, refractive index contrast and suspension concentration, the PSA can also be employed in biological applications. To this end, the second part of the thesis aims to optimise the PSA for the measurement of small (< 10 µm) particles such as microorganisms. The results demonstrate that the modified PSA in combination with ML is able to accurately classify different types of bacteria (Escherichia coli and Enterococcus sp.) and distinguish them from silica beads of comparable sizes, with an accuracy of 89%. Moreover, it can detect the concentration of bacteria in water with a limit of detection (LOD) of approximately 105 cells mL-1. The final part of the thesis is dedicated to the development of a low-cost, portable optical biosensor for the specific detection of particles smaller than bacteria, such as viruses (< 1 µm). The proposed system, which we have called flow virometry reader (FVR), is a modification of a flow cytometer and relies on measuring light emissions from fluorescent antibodies that bind to specific viral particles. An LOD of 3,834 copies mL-1 for SARS-CoV-2 in saliva can be achieved with the device. The FVR is clinically validated using 54 saliva samples in a blind test, with high sensitivity and specificity of 91.2% and 90%, respectively. These findings suggest that the FVR has the potential to be a highly viable alternative to current diagnostic methods for pandemic events, as it is faster (< 30 min) and less expensive than PCR tests, while being more sensitive than today’s COVID-19 rapid antigen tests. The photonic sensing technologies developed in the thesis show significant potential for use in a wide range of applications, including: • particulate air pollution, causing cardiovascular and respiratory problems • particulate water pollution, which affects the ecosystems of rivers, lakes and oceans • total bacterial count in environmental or bathing water • viral pandemics The technologies are particularly appealing in countries with limited resources due to their simple design, portability, short time-to-result and affordability, as well as the fact that they do not require a specialised laboratory or trained personnel to operate them.El objetivo de esta tesis es el diseño, desarrollo y validación de dos nuevos sensores fotónicos para la detección y caracterización de muestras industriales y biológicas. El primero es un PSA en configuración de haz colimado que usa un innovador filtro espacial angular y una cámara electrónica similar a la usada en móviles. El pequeño factor de tamaño del filtro angular espacial permite la detección de la luz difusa de las partículas hasta ángulos discretos predefinidos. A partir del uso de imágenes difusas angularmente resueltas obtenidas por la cámara, un algoritmo de aprendizaje automático, machine learning (ML) en inglés, puede predecir la mediana del diámetro del volumen de las partículas. Nuestro sistema ha conseguido un error absoluto medio porcentual de solamente un 0.72% para partículas esféricas en disoluciones con tamaños superiores a 10 µm y concentraciones de hasta 40 mg mL-1. En comparación a sistemas tradicionales de difracción láser, el propuesto PSA es un orden de magnitud más pequeño en tamaño, peso y coste, y ofrece un enfoque prometedor para la supervisión online de procesos industriales. Dado que la difusión de luz depende de más factores aparte del tamaño de la partícula, incluyendo la forma, el contraste del índice de refracción y la suspensión de la concentración, el PSA también puede ser empleado en aplicaciones biológicas. Con este objetivo, la segunda parte de la tesis busca optimizar el PSA para la medida de partículas pequeñas (< 10 µm) como microorganismos. Los resultados demuestran que el PSA modificado en combinación con ML es capaz de clasificar con exactitud diferentes tipos de bacterias (Escherichia coli y Enterococcus sp.) y diferéncialas de partículas de silicio con tamaños similares, con una precisión del 89%. Además, puede detectar una concentración de bacterias en agua con un límite de detección (LOD en inglés) de aproximadamente 105 células mL-1. La parte final de tesis está dedicada al desarrollo de un biosensor óptico de bajo coste y portátil para la detección especifica de partículas más pequeñas que bacterias, como virus (< 1 µm). El sistema propuesto, el cual hemos llamado flow virometry reader (FVR), es una modificación de un citómetro de flujo y se basa en la medida de emisiones de luz provenientes de anticuerpos fluorescentes que son unidos a partículas virales específicas. Con este dispositivo se puede conseguir un LOD de 3,834 copias mL-1 para el SARS-CoV-2 en saliva. El FVR ha sido validado clínicamente usando 54 muestras de saliva en un test a ciegas, con una sensibilidad y especificidad del 91.2% y 90%, respectivamente. Estos hallazgos sugieren que el FVR tiene el potencial de ser una alternativa viable a los métodos de diagnóstico actuales en escenarios de pandemias, pues es rápido (< 30 min) y menos costoso que los test por PCR, mientras que es más sensible que los actuales test de antígenos para COVID-19. Las tecnologías de detección fotónicas desarrolladas en esta tesis muestran un potencial significativo para su uso en un amplio rango de aplicaciones, incluyendo: -contaminación de aire por partículas, causantes de problemas cardiovasculares y respiratorios -contaminación de agua por partículas, el cual afecta a ecosistemas como ríos, lagos y océanos -recuento total de bacterias en aguas de baño o ambientales -pandemias víricas. Estas tecnologías son particularmente atractivas en países con recursos limitados, dado sus simples diseños, portabilidad, el poco tiempo de espera para obtener resultados y asequibilidad, así como el hecho de que estos no requieren un laboratorio especializado o un personal cualificado para operar con ellas.Postprint (published version

Rapid classification of micro-particles using multi-angle dynamic light scatting and machine learning approach

The rapid classification of micro-particles has a vast range of applications in biomedical sciences and technology. In the given study, a prototype has been developed for the rapid detection of particle size using multi-angle dynamic light scattering and a machine learning approach by applying a support vector machine. The device consisted of three major parts: a laser light, an assembly of twelve sensors, and a data acquisition system. The laser light with a wavelength of 660 nm was directed towards the prepared sample. The twelve different photosensors were arranged symmetrically surrounding the testing sample to acquire the scattered light. The position of the photosensor was based on the Mie scattering theory to detect the maximum light scattering. In this study, three different spherical microparticles with sizes of 1, 2, and 4 μm were analyzed for the classification. The real-time light scattering signals were collected from each sample for 30 min. The power spectrum feature was evaluated from the acquired waveforms, and then recursive feature elimination was utilized to filter the features with the highest correlation. The machine learning classifiers were trained using the features with optimum conditions and the classification accuracies were evaluated. The results showed higher classification accuracies of 94.41%, 94.20%, and 96.12% for the particle sizes of 1, 2, and 4 μm, respectively. The given method depicted an overall classification accuracy of 95.38%. The acquired results showed that the developed system can detect microparticles within the range of 1–4 μm, with detection limit of 0.025 mg/ml. Therefore, the current study validated the performance of the device, and the given technique can be further applied in clinical applications for the detection of microbial particles

Alkynyl N-BODIPYs as Reactive Intermediates for the Development of Dyes for Biophotonics

A new approach for the rapid multi-functionalization of BODIPY dyes towards biophotonics is reported. It is based on novel N-BODIPYs, through reactive intermediates with alkynyl groups to be further derivatized by click chemistry. This approach has been exemplified by the development of new dyes for cell bio-imaging, which have proven to successfully internalize into pancreatic cancer cells and accumulate in the mitochondria. The in vitro suitability for photodynamic therapy (PDT) was also analyzed and confirmed our compounds to be promising PDT candidates for the treatment of pancreatic cancer

Biomedical Applications of Mid-Infrared Spectroscopic Imaging and Multivariate Data Analysis: Contribution to the Understanding of Diabetes Pathogenesis

Diabetic retinopathy (DR) is a microvascular complication of diabetes and a leading cause of adult vision loss. Although a great deal of progress has been made in ophthalmological examinations and clinical approaches to detect the signs of retinopathy in patients with diabetes, there still remain outstanding questions regarding the molecular and biochemical changes involved. To discover the biochemical mechanisms underlying the development and progression of changes in the retina as a result of diabetes, a more comprehensive understanding of the bio-molecular processes, in individual retinal cells subjected to hyperglycemia, is required. Animal models provide a suitable resource for temporal detection of the underlying pathophysiological and biochemical changes associated with DR, which is not fully attainable in human studies. In the present study, I aimed to determine the nature of diabetes-induced, highly localized biochemical changes in the retinal tissue from Ins2Akita/+ (Akita/+; a model of Type I diabetes) male mice with different duration of diabetes. Employing label-free, spatially resolved Fourier transform infrared (FT-IR) imaging engaged with chemometric tools enabled me to identify temporal-dependent reproducible biomarkers of the diabetic retinal tissue from mice with 6 or 12 weeks, and 6 or 10 months of diabetes. I report, for the first time, the origin of molecular changes in the biochemistry of individual retinal layers with different duration of diabetes. A robust classification between distinctive retinal layers - namely photoreceptor layer (PRL), outer plexiform layer (OPL), inner nuclear layer (INL), and inner plexiform layer (IPL) - and associated temporal-dependent spectral biomarkers, were delineated. Spatially-resolved super resolution chemical images revealed oxidative stress-induced structural and morphological alterations within the nucleus of the photoreceptors. Comparison among the PRL, OPL, INL, and IPL suggested that the photoreceptor layer is the most susceptible layer to the oxidative stress with short-duration of diabetes. Moreover, for the first time, we present the temporal-dependent molecular alterations for the PRL, OPL, INL, and IPL from Akita/+ mice, with progression of diabetes. These findings are potentially important and may be of particular benefit in understanding the molecular and biological activity of retinal cells during oxidative stress in diabetes. Our integrating paradigm provides a new conceptual framework and a significant rationale for a better understanding of the molecular and cellular mechanisms underlying the development and progression of DR. This approach may yield alternative and potentially complimentary methods for the assessment of diabetes changes. It is expected that the conclusions drawn from this work will bridge the gap in our knowledge regarding the biochemical mechanisms of the DR and address some critical needs in the biomedical community

Recent trends in smartphone-based detection for biomedical applications: a review

Smartphone-based imaging devices (SIDs) have shown to be versatile and have a wide range of biomedical applications. With the increasing demand for high-quality medical services, technological interventions such as portable devices that can be used in remote and resource-less conditions and have an impact on quantity and quality of care. Additionally, smartphone-based devices have shown their application in the field of teleimaging, food technology, education, etc. Depending on the application and imaging capability required, the optical arrangement of the SID varies which enables them to be used in multiple setups like bright-field, fluorescence, dark-field, and multiple arrays with certain changes in their optics and illumination. This comprehensive review discusses the numerous applications and development of SIDs towards histopathological examination, detection of bacteria and viruses, food technology, and routine diagnosis. Smartphone-based devices are complemented with deep learning methods to further increase the efficiency of the devices. [Figure not available: see fulltext.] © 2021, The Author(s)

Application of Plasmonic Nanostructures in Molecular Diagnostics and Biosensor Technology: Challenges and Current Developments

The recent global pandemic caused by Covid-19 enforced the urgent need for accessible, reliable, and accurate point-of-care rapid diagnostics based on plasmonic nanostructures. This is because fast and reliable testing was the key driver in curbing the spread of Covid-19. The traditional methods of diagnostics and biosensors often require expensive infrastructure and highly qualified and trained personnel, which limits their accessibility. These limitations perpetuated the impact of Covid-19 in most countries because of the lack of easily accessible point-of-care rapid diagnostic kits. This review revealed that portable and reliable point-of-care diagnostic kits are very crucial in reaching large populations, especially in underdeveloped and developing countries. This gives perspective to novel point-of-care applications. Furthermore, water quality is a very crucial part of food safety, especially in developing countries faced with water contamination. In this chapter, we explored the various challenges and recent developments in the use of plasmonic nanostructures for application in molecular diagnostics and biosensing for the detection of infectious diseases and common environmental pathogens

New methods in Palaeopalynology: Classification of pollen through pollen chemistry

Pollen grains are one of the primary tools of palaeoecologists to reconstruct vegetation changes in the past. The description, counting and analysis of pollen grains (palynology) has contributed to our understanding of establishment and dynamics of past and present plant communities. Advances in identification accuracy, precision and increased taxonomic resolution have greatly improved our understanding of biogeography and plant community interactions. Nevertheless, the techniques by which palynological studies are performed have not fundamentally changed. Taxonomic resolution and automation have been identified as some of the key challenges for palynology and palaeoecology. Chemical methods have been proposed as a potential alternative to morphological approaches and have demonstrated promising results in the classification of modern pollen grains and in the analysis of pollen chemical responses to UV-B radiation. The application of chemical methods for palynological needs have not been thoroughly explored, with analysis of (sub-)fossil pollen lagging behind their modern counterpart. Especially the application of infrared methods have gained popularity as an alternative to traditional morphological approaches. In this thesis, I explore the use of infrared methods for palynological applications, by exploring the chemical variation in modern pollen grains and in the analysis of fossil pollen grains with IR microscope approaches. The objectives of this thesis are formulated into three research objectives: * Collect modern pollen and explore the variation in chemical composition * Apply chemical methods to fossil material * Explore microscopy chemical methods on modern pollen The thesis is structured into four studies to study these objectives. Papers I and II explore variation and classification based on the chemical composition of modern *Quercus* pollen using two IR approaches, Fourier transform infrared spectroscopy (FTIR) and Fourier transform Raman spectroscopy (FT-Raman). After exploring modern chemical composition of pollen, paper III investigates FTIR methods for the analysis of fossil pollen, in spectra of Holocene *Pinus* pollen. Additionally, the effects of acetolysis and density separation on *Pinus* pollen is described. Paper IV addresses the challenge of scattering signals when measuring small pollen grains of four *Quercus* species with FTIR microscopy and ways to surpress or weaken the scattering signals. The results from paper I and II show classification success, surpassing traditional morphological approaches, at the *Quercus* section level and ~90% recall on species level with both IR approaches. Chemical bands most useful for classification are lipids, sporopollenin and proteins for both FT-Raman and FTIR. We observe differences in the importance of chemical functional groups for the classification. FT-Raman relies more on sporopollenin chemistry, while FTIR utilizes more variation in lipid bands. After finding considerable variation in sporopollenin chemistry in modern pollen samples, FTIR methods were applied to pollen from sediment cores spanning the Holocene. Paper III examines the differences between modern and sub-fossil pollen and reported large differences between them, mainly the removal of labile components, such as lipids and protein peaks from the sub-fossil spectra during diagenesis. Additionally, paper III finds changes to pollen chemistry caused by acetolysis in the 1200 - 1000 cm^-1^ region of the spectra, when comparing acetolysed spectra to non-acetolysed spectra. The paper concludes with findings of unwanted inorganic signals (BSi) and contamination from density separation media in the sediment pollen spectra. Paper IV demonstrates two successful methods of removing scattering signals from pollen spectra. Two approaches were examined, embedding and processing with signal correction algorithms. Spectra from embedded pollen have no scattering anomalies, but part of the spectra is unusable, because of absorbance of the embedding matrix (paraffin). The signal processing algorithm removes most of the scatter components and allows the scatter components to be extracted. Classification of the different data-sets (spectra without correction, embedded spectra, processed spectra, scatter parameters) reveals that scatter correction methods reduce classification success and that scatter parameters contain taxonomic information. This suggests that scatter corrections may not be the best approach for applications mainly focused on classification or identification, while reconstructions of, for example, UV-B radiation may benefit from scatter correction methods, when measuring single grain spectra. This thesis shows that the performance of IR methods surpasses traditional morphological methods for pollen classification and that a considerable amount of taxonomic information is stored in functional groups associated with sporopollenin (phenylpropanoids). In a study on fossil pollen, this thesis demonstrates that conventional chemical extraction methods, such as acetolysis, alter the chemical composition of pollen and may not be ideal for palaeochemical purposes. Additionally, the scatter correction methods show that IR can provide non-chemical information in the form of scatter parameters, which contain taxonomic information. These results are useful additions to the growing knowledge on chemical methods for palaeoecological and palynological analyses.Doktorgradsavhandlin

Diagnostic Biosensors for Detection of Blood-Derived Biomarkers

Standard diagnostic tools used from patient samples, specifically from blood draws, require specialized equipment, personnel, and facilities. Conventional techniques can often be very laborious and time consuming due to required sample preparation. The evident delay from sample collection to a patient’s result immensely impacts their outcome. The aims of this research are to design diagnostic biosensors that decrease time-to-results, minimize reagent and sample handling, and incorporate automated simple optical transduction and user interfaces for the detection of blood-derived biomarkers. Specifically, four biosensing detection mechanisms performed on 3 different point-of-care platforms will be discussed. First is a static loop-mediated isothermal amplification (LAMP) of nucleic acid aqueous droplet on a silicone chip platform immersed in mineral oil. The target-of-interest is a nucleic acid sequence as a biomarker for antibiotic resistant bacteria. The biosensing technique used related changes in interfacial tension (IFT) at the water-oil interface by measuring the change in contact angle (geometrical-effects) over time. Initially the system was characterized as a linear response in relation to concentration of bacteria in a buffer system down to the limit of detection (LOD) of 100 CFU per uL. Subsequently, with the addition of bacterial infected blood sample models, the system became a binary assay (i.e. yes or no) as low as 10 CFU per uL within 10 min of reaction. Secondly, a two-layered, paper microfluidic chip was utilized to quantify cancer cells from a buffy coat sample matrix by two detection mechanisms: 1) on-chip particle enumeration via smartphone microscope and 2) capillary flow dynamics via smartphone video processing. The assay resulted in a LOD as low as 1 cell per uL for the on-chip imaging aspect of platform and 0.1 cell per uL for the capillary flow analysis within 13 to 22s post application of blood sample. Lastly, the same concepts previously described in the first platform utilizes changes in IFT due to amplicon presence in an aqueous solution immersed in mineral oil. An emulsion LAMP platform was investigated to determine the relation between angle-dependent light scatter intensity (based off Mie scatter theory) and nucleic acid amplification progression. The phenomenon attributing to changes in light scatter intensities is due to the interfacial changes occurring in the emulsion droplets, where amplicon amount increases the IFT decreases, resulting in smaller diameter emulsions. Changes in light scatter intensity within 3 min of the reaction shows statistical difference in comparison to no target control (NTC) for 10^3 CFU per uL of bacteria dosed into aqueous sample. These four detection mechanisms and three platforms offer but a few alternatives as biosensing methods for blood-derived diagnostic biosensors

Recent advances in low-cost particulate matter sensor: calibration and application

Particulate matter (PM) has been monitored routinely due to its negative effects on human health and atmospheric visibility. Standard gravimetric measurements and current commercial instruments for field measurements are still expensive and laborious. The high cost of conventional instruments typically limits the number of monitoring sites, which in turn undermines the accuracy of real-time mapping of sources and hotspots of air pollutants with insufficient spatial resolution. The new trends of PM concentration measurement are personalized portable devices for individual customers and networking of large quantity sensors to meet the demand of Big Data. Therefore, low-cost PM sensors have been studied extensively due to their price advantage and compact size. These sensors have been considered as a good supplement of current monitoring sites for high spatial-temporal PM mapping. However, a large concern is the accuracy of these low-cost PM sensors. Multiple types of low-cost PM sensors and monitors were calibrated against reference instruments. All these units demonstrated high linearity against reference instruments with high R2 values for different types of aerosols over a wide range of concentration levels. The question of whether low-cost PM monitors can be considered as a substituent of conventional instruments was discussed, together with how to qualitatively describe the improvement of data quality due to calibrations. A limitation of these sensors and monitors is that their outputs depended highly on particle composition and size, resulting in as high as 10 times difference in the sensor outputs. Optical characterization of low-cost PM sensors (ensemble measurement) was conducted by combining experimental results with Mie scattering theory. The reasons for their dependence on the PM composition and size distribution were studied. To improve accuracy in estimation of mass concentration, an expression for K as a function of the geometric mean diameter, geometric standard deviation, and refractive index is proposed. To get rid of the influence of the refractive index, we propose a new design of a multi-wavelength sensor with a robust data inversion routine to estimate the PM size distribution and refractive index simultaneously. The utility of the networked system with improved sensitivity was demonstrated by deploying it in a woodworking shop. Data collected by the networked system was utilized to construct spatiotemporal PM concentration distributions using an ordinary Kriging method and an Artificial Neural Network model to elucidate particle generation and ventilation processes. Furthermore, for the outdoor environment, data reported by low-cost sensors were compared against satellite data. The remote sensing data could provide a daily calibration of these low-cost sensors. On the other hand, low-cost PM sensors could provide better accuracy to demonstrate the microenvironment