Significance: Biologists have long sought to understand how differences in the structure of vertebrate respiratory systems are related to differences in oxygen consumption rates associated with metabolism. In particular, they have sought to understand how structural constraints on passive oxygen diffusion through gills or lungs are related to the nonlinear body mass scaling of oxygen consumption rates. Here we show that the body mass dependence of oxygen diffusion capacity, which is governed by the area and thickness of respiratory surfaces, matches the body mass dependence of respiration in diverse endothermic (birds and mammals) and ectothermic (reptiles, amphibians, and fishes) vertebrates. These results provide a step toward a more complete understanding of the structural differences that underlie changes in oxygen consumption rates with body size.

Impedimetric biosensors for measuring small molecules based on weak/transient interactions between bioreceptors and target analytes are a challenge for detection electronics, particularly in field studies or in the analysis of complex matrices. Protein–ligand binding sensors have enormous potential for biosensing, but achieving accuracy in complex solutions is a major challenge. There is a need for simple post hoc analytical tools that are not computationally expensive, yet provide near real time feedback on data derived from impedance spectra. Here, we show the use of a simple, open source support vector machine learning algorithm for analyzing impedimetric data in lieu of using equivalent circuit analysis. We demonstrate two different protein-based biosensors to show that the tool can be used for various applications. We conclude with a mobile phone-based demonstration focused on the measurement of acetone, an important biomarker related to the onset of diabetic ketoacidosis. In all conditions tested, the open source classifier was capable of performing as well as, or better, than the equivalent circuit analysis for characterizing weak/transient interactions between a model ligand (acetone) and a small chemosensory protein derived from the tsetse fly. In addition, the tool has a low computational requirement, facilitating use for mobile acquisition systems such as mobile phones. The protocol is deployed through Jupyter notebook (an open source computing environment available for mobile phone, tablet or computer use) and the code was written in Python. For each of the applications, we provide step-by-step instructions in English, Spanish, Mandarin and Portuguese to facilitate widespread use. All codes were based on scikit-learn, an open source software machine learning library in the Python language, and were processed in Jupyter notebook, an open-source web application for Python. The tool can easily be integrated with the mobile biosensor equipment for rapid detection, facilitating use by a broad range of impedimetric biosensor users. This post hoc analysis tool can serve as a launchpad for the convergence of nanobiosensors in planetary health monitoring applications based on mobile phone hardware.

For the first time, we combine pulsed electrodeposition with out-of-phase pulsed sonication for controlled synthesis of fractal nanoplatinum structures as the transducer layer in electrochemical sensing. We develop and test this technique, called bimodal pulsed sonoelectrodeposition (pulSED), as a simple approach for creating highly conductive transducer nanometals for use in sensing and biosensing. We first compared the efficiency of pulSED nanoplatinum to other pulsed electrodeposition techniques, and then explored the effect of duty cycle and plating time on electroactive surface area and nanoparticle size/morphology. The developed pulSED nanoplatinum displayed fractal features with a relatively homogenous size distribution (26.31 ± 1.3 nm) and extremely high electroactive surface (0.28 ± 0.04 cm²) relative to other electroplating techniques (up to one order of magnitude higher). A high duty cycle (900 mHz) promotes formation of stable nanostructures (including fractal nanostructures) and reduces amorphous structure formation due to bubble cavitation and enhanced mass transport of metal ions to the electrode surface. To demonstrate the applicability of the pulSED technique, non-enzymatic and enzymatic sensors were developed for measuring hydrogen peroxide and glucose. The sensitivity for non-enzymatic peroxide sensing (3335 ± 305 μA cm⁻² mM⁻¹), non-enzymatic glucose sensing (73 ± 14 μA cm⁻² mM⁻¹) and enzymatic glucose biosensing (155 ± 25 μA cm⁻² mM⁻¹) was higher than, or similar to, other nanomaterial-mediated amperometric sensors reported in the literature. The pulSED technique is a one pot method for tunable synthesis of nanometal structures as a transducer layer in electrochemical sensing and biosensing that requires no precursors or capping agents, and can be carried out at room temperature with inexpensive hardware.

In this review, we discuss the role of sensor analytics point solutions (SNAPS), a reduced complexity machine-assisted decision support tool. We summarize the approaches used for mobile phone-based chemical/biological sensors, including general hardware and software requirements for signal transduction and acquisition. We introduce SNAPS, part of a platform approach to converge sensor data and analytics. The platform is designed to consist of a portfolio of modular tools which may lend itself to dynamic composability by enabling context-specific selection of relevant units, resulting in case-based working modules. SNAPS is an element of this platform where data analytics, statistical characterization and algorithms may be delivered to the data either via embedded systems in devices, or sourced, in near real-time, from mist, fog or cloud computing resources. Convergence of the physical systems with the cyber components paves the path for SNAPS to progress to higher levels of artificial reasoning tools (ART) and emerge as data-informed decision support, as a service for general societal needs. Proof of concept examples of SNAPS are demonstrated both for quantitative data and qualitative data, each operated using a mobile device (smartphone or tablet) for data acquisition and analytics. We discuss the challenges and opportunities for SNAPS, centered around the value to users/stakeholders and the key performance indicators users may find helpful, for these types of machine-assisted tools.