Lucas de Brito Ayres

Bio: Lucas de Brito Ayres is an academic researcher from Clemson University. The author has contributed to research in topics: Wearable computer & Biosensor. The author has an hindex of 2, co-authored 2 publications receiving 16 citations. Previous affiliations of Lucas de Brito Ayres include University of São Paulo.

Integrated instrumental analysis teaching platform with smartphone-operated fluorometer

Abstract: The present work describes an Integrated Teaching Tool (ITT) to facilitate the learning process in analytical chemistry. The first instrument integrated in the platform to demonstrate the concept is a wireless, portable fluorometer, produced by 3D printing. The low-cost instrument features a Teensy 3.1 board as the microcontroller, a high-power UV-LED, a secondary filter, a photodiode, and simple auxiliary electronic circuits. Modules of the ITT app were designed to manage the instrument and perform data acquisition remotely from any Android smartphone via Bluetooth, plot and transmit the results. Supporting the educational purpose of the platform, examples of basic concepts about fluorescence as well as technical information about the instrument are also provided to be considered for the app, which also allows instructors to assist and evaluate students through push notifications.

Rapid Detection of Staphylococcus aureus Using Paper-Derived Electrochemical Biosensors.

Abstract: Several groups have recently explored the idea of developing electrochemical paper-based wearable devices, specifically targeting metabolites in sweat. While these sensors have the potential to provide a breadth of analytical information, there are several key challenges to address before these sensors can be widely adopted for clinical interventions. Toward this goal, we describe the development of a paper-based electrochemical sensor for the detection of Staphylococcus aureus. Enabling the application, this report describes the use of paper-derived carbon electrodes, which were modified with a thin layer of sputtered gold (that minimizes lateral resistivity and significantly improves the electron transfer process) and with chitosan (used as a binder, to offer flexibility). The resulting material was laser-patterned and applied for the development of an electrochemical biosensor controlled (via a wireless connection) by a custom-built, portable potentiostat. As no interference was observed when exposed to other bacteria or common metabolites, this wearable system (paper-derived electrodes + potentiostat) has the potential to detect the presence of S. aureus in the skin, a commonly misdiagnosed and mistreated infection.

Artificial Intelligence: A Powerful Paradigm for Scientific Research

Abstract: Artificial Intelligence (AI) coupled with promising machine learning (ML) techniques well known from computer science is broadly affecting many aspects of various fields including science and technology, industry, and even our day to day life. The ML techniques have been developed to analyze high-throughput data with a view to obtaining useful insights, categorizing, predicting and making evidence-based decisions in novel ways, which will promote the growth of novel applications and fuel the sustainable booming of AI. This paper undertakes performs a comprehensive survey on the development and application of AI in different aspects of fundamental sciences, including information science, mathematics, medical science, materials science, geoscience, life science, physics and chemistry. The challenges that each discipline of science meets, and the potentials of AI techniques to handle these challenges, are discussed in detail. Moreover, we shed light on new research trends entailing the integration of AI into each scientific discipline. The goal of this paper is to provide a broad research guideline on fundamental sciences with potential infusion of AI, to help motivate researchers to deeply understand the state-of-the-art applications of AI-based fundamental sciences, and thereby to help promote the continuous development of these fundamental sciences.

Explainable Deep Learning-Assisted Fluorescence Discrimination for Aminoglycoside Antibiotic Identification.

Abstract: The complexity and multivariate analysis of biological systems and environment are the drawbacks of the current high-throughput sensing method and multianalyte identification. Deep learning (DL) algorithms contribute a big advantage in analyzing the nonlinear and multidimensional data. However, most DL models are data-driven black boxes suffering from nontransparent inner workings. In this work, we developed an explainable DL-assisted visualized fluorometric array-based sensing method. Based on a data set of 8496 fluorometric images of various target molecule fingerprint patterns, two typical DL algorithms and eight machine learning algorithms were investigated for the efficient qualitative and quantitative analysis of six aminoglycoside antibiotics (AGs). The convolutional neural network (CNN) approached 100% prediction accuracy and 1.34 ppm limit of detection of six AG analysis in domestic, industrial, medical, consumption, or aquaculture water. The class activation mapping assessment explicates how the CNN model assesses the importance of sensor elements and makes the discrimination decision. The feedback mechanism guides the sensor array evolution for less material using a simplified operation or efficient data acquisition. The explainable DL-assisted analysis method establishes an "end-to-end" strategy to resolve the black box of the DL algorithm, promote hardware design or principle optimization, and contribute facile indicators for environment monitoring, disease diagnosis, and even new scientific discovery.

Biocompatible Wearable Electrodes on Leaves toward the On-Site Monitoring of Water Loss from Plants.

Abstract: Impedimetric wearable sensors are a promising strategy for determining the loss of water content (LWC) from leaves because they can afford on-site and nondestructive quantification of cellular water from a single measurement. Because the water content is a key marker of leaf health, monitoring of the LWC can lend key insights into daily practice in precision agriculture, toxicity studies, and the development of agricultural inputs. Ongoing challenges with this monitoring are the on-leaf adhesion, compatibility, scalability, and reproducibility of the electrodes, especially when subjected to long-term measurements. This paper introduces a set of sensing material, technological, and data processing solutions that overwhelm such obstacles. Mass-production-suitable electrodes consisting of stand-alone Ni films obtained by well-established microfabrication methods or ecofriendly pyrolyzed paper enabled reproducible determination of the LWC from soy leaves with optimized sensibilities of 27.0 (Ni) and 17.5 kΩ %-1 (paper). The freestanding design of the Ni electrodes was further key to delivering high on-leaf adhesion and long-term compatibility. Their impedances remained unchanged under the action of wind at velocities of up to 2.00 m s-1, whereas X-ray nanoprobe fluorescence assays allowed us to confirm the Ni sensor compatibility by the monitoring of the soy leaf health in an electrode-exposed area. Both electrodes operated through direct transfer of the conductive materials on hairy soy leaves using an ordinary adhesive tape. We used a hand-held and low-power potentiostat with wireless connection to a smartphone to determine the LWC over 24 h. Impressively, a machine-learning model was able to convert the sensing responses into a simple mathematical equation that gauged the impairments on the water content at two temperatures (30 and 20 °C) with reduced root-mean-square errors (0.1% up to 0.3%). These data suggest broad applicability of the platform by enabling direct determination of the LWC from leaves even at variable temperatures. Overall, our findings may help to pave the way for translating "sense-act" technologies into practice toward the on-site and remote investigation of plant drought stress. These platforms can provide key information for aiding efficient data-driven management and guiding decision-making steps.