Javier Sotres

Bacterial infections can affect the skin, lungs, blood, and brain, and are among the leading causes of mortality globally. Early infection detection is critical in diagnosis and treatment but is a time- and work-consuming process taking several days, creating a hitherto unmet need to develop simple, rapid, and accurate methods for bacterial detection at the point of care. The most frequent type of bacterial infection is infection of the urinary tract. Here, we present a wireless-enabled, portable, potentiometric sensor for E. coli. E. coli was chosen as a model bacterium since it is the most common cause of urinary tract infections. The sensing principle is based on reduction of Prussian blue by the metabolic activity of the bacteria, detected by monitoring the potential of the sensor, transferring the sensor signal via Bluetooth, and recording the output on a laptop or a mobile phone. In sensing of bacteria in an artificial urine medium, E. coli was detected in ~4 h (237 ± 19 min; n = 4) and in less than 0.5 h (21 ± 7 min, n = 3) using initial E. coli concentrations of ~10³ and 10⁵ cells mL⁻¹, respectively, which is under or on the limit for classification of a urinary tract infection. Detection of E. coli was also demonstrated in authentic urine samples with bacteria concentration as low as 10⁴ cells mL⁻¹, with a similar response recorded between urine samples collected from different volunteers as well as from morning and afternoon urine samples.

Atomic Force Microscopy (AFM) force measurements are a powerful tool for the nano-scale characterization of surface properties. However, the analysis of force measurements requires several processing steps. One is locating different type of events e.g., contact point, adhesions and indentations. At present, there is a lack of algorithms that can automate this process in a reliable way for different types of samples. Moreover, because of their stochastic nature, the acquisition and analysis of a high number of force measurements is typically required. This can result in these experiments becoming an overwhelming task if their analysis is not automated. Here, we propose a Machine Learning approach, the use of one-dimensional convolutional neural networks, to locate specific events within AFM force measurements. Specifically, we focus on locating the contact point, a critical step for the accurate quantification of mechanical properties as well as long-range interactions. We validate this approach on force measurements obtained both on hard and soft surfaces. This approach, which could be easily used to also locate other events e.g., indentations and adhesions, has the potential to significantly facilitate and automate the analysis of AFM force measurements and, therefore, the use of this technique by a wider community.

To maximize the potential of 5G infrastructure in healthcare, simple integration of biosensors with wireless tag antennas would be beneficial. This work introduces novel glucose-to-resistor transduction, which enables simple, wireless biosensor design. The biosensor was realized on a near-field communication tag antenna, where a sensing bioanode generated electrical current and electroreduced a nonconducting antenna material into an excellent conductor. For this, a part of the antenna was replaced by a Ag nanoparticle layer oxidized to high-resistance AgCl. The bioanode was based on Au nanoparticle-wired glucose dehydrogenase (GDH). The exposure of the cathode-bioanode to glucose solution resulted in GDH-catalyzed oxidation of glucose at the bioanode with a concomitant reduction of AgCl to highly conducting Ag on the cathode. The AgCl-to-Ag conversion strongly affected the impedance of the antenna circuit, allowing wireless detection of glucose. Mimicking the final application, the proposed wireless biosensor was ultimately evaluated through the measurement of glucose in whole blood, showing good agreement with the values obtained with a commercially available glucometer. This work, for the first time, demonstrates that making a part of the antenna from the AgCl layer allows achieving simple, chip-less, and battery-less wireless sensing of enzyme-catalyzed reduction reaction.

Surfactants are important components of oral care products. Sodium dodecyl sulfate (SDS) is the most common because of its foaming properties, taste and low cost. However, the use of ionic surfactants, especially SDS, is related to several oral mucosa conditions. Thus, there is a high interest in using non-ionic and amphoteric surfactants as they are less irritant. To better understand the performance of these surfactants in oral care products, we investigated their interaction with salivary pellicles i.e., the proteinaceous films that cover surfaces exposed to saliva. Specifically, we focused on pentaethylene glycol monododecyl ether (C₁₂E₅) and cocamidopropyl betaine (CAPB) as model nonionic and amphoteric surfactants respectively, and investigated their interaction with reconstituted salivary pellicles with various surface techniques: Quartz Crystal Microbalance with Dissipation, Ellipsometry, Force Spectroscopy and Neutron Reflectometry. Both C₁₂E₅ and CAPB were gentler on pellicles than SDS, removing a lower amount. However, their interaction with pellicles differed. Our work indicates that CAPB would mainly interact with the mucin components of pellicles, leading to collapse and dehydration. In contrast, exposure to C₁₂E₅ had a minimal effect on the pellicles, mainly resulting in the replacement/solubilisation of some of the components anchoring pellicles to their substrate.

Use of state-of-the-art deep learning techniques, object detectors and Siamese networks, to enable autonomous SPM imaging of single molecules.

Proteases are often used as biomarkers of many pathologies as well as of microbial contamination and infection. Therefore, extensive efforts are devoted to the development of protease sensors. Some applications would benefit from wireless monitoring of proteolytic activity at minimal cost e.g., sensors embedded in care products like wound dressings and diapers to track wound and urinary infections. Passive (battery-less) and chip-less transponders stands out among wireless sensing technologies when low-cost is a requirement. Here we developed and extensively characterized a biodegradable but still highly stable in aqueous media composite material whose proteolytic degradation could be used in these wireless transponders as a transduction mechanism of proteolytic activity. This composite material consisted on a cross-linked gelatin network with incorporated caprylic acid. The digestion of the composite when exposed to proteases results in a change of its resistivity, a quantity that can be wirelessly monitored by coupling the composite to an inductor-capacitor resonator i.e., an antenna. We experimentally proved this wireless sensor concept by monitoring the presence of a variety of proteases in aqueous media. Moreover, we also showed that detection time follows a relationship with protease concentration, which enables quantification possibilities for practical applications.

Material scientist are in need of experimental techniques that facilitate a quantitative mechanical characterization of mesoscale materials and, therefore, their rational design. An example is that of thin organic films, as their performance often relates to their ability to withstand use without damage. The mechanical characterization of thin films has benefited from the emergence of the Atomic Force Microscope (AFM). In this regard, it is of relevance that most soft materials are not elastic but viscoelastic instead. While most AFM operation modes and analysis procedures are suitable for elasticity studies, the use of AFM for quantitative viscoelastic characterizations is still a challenge. This is now an emerging topic due to recent developments in Contact Resonance AFM. The aim of this work was to further explore the potential of this technique by investigating its sensitivity to viscoelastic changes induced by environmental parameters, specifically humidity. Here we show that by means of this experimental approach it was possible to quantitatively monitor the influence of humidity on the viscoelasticity of two different thin and hydrophobic polyurethane coatings representative of those typically used to protect materials from processes like weathering and wear. The technique was sensitive even to the transition between the anti-plasticizing and plasticizing effects of ambient humidity. Moreover, we showed that this was possible without the need of externally exciting the AFM cantilever or the sample i.e., just by monitoring the Brownian motion of cantilevers, which significantly facilitates the implementation of this technique in any AFM setup.