Electrochemical investigation of edible oils: Experimentation, electrical signatures, and a supervised learning-case study of adulterated peanut oils.

Traditional approaches to characterize edible oils such as chemical, chromatographic and light absorption techniques are laborious, expensive, and bulky to implement. This paper presents the electrochemical impedance spectroscopy of 13 types of edible oils, a rapid robust approach to characterizing the electrical behavior of oils without sample preparation. This is achieved through probing the oils via oscillating electric fields to capture oil-specific electrical behaviors. The principal component analysis discriminates the oil types well and establishes repetitive behavioral trends, perceived as electrical signatures. This data is applied in a case study of adulterated peanut oils to quantify adulteration via supervised machine learning with batch-wise leave-one-out implementation. The mean absolute errors and R2 values measure 2.18-3.27 and 0.975-0.991 respectively across 4 test batches. This work provides an exemplar for the electrochemical study of edible oils, with potential for portable proof-of-value device configurations for rapid in situ analysis of edible oils and adulterated oils.

Machine learning-based predictive analysis of total polar compounds (TPC) content in frying oils: A comprehensive electrochemical study of 6 types of frying oils with various frying timepoints.

Standard approaches to determining the total polar compounds (TPC) content in frying oils such as the chromatographic techniques are slow, bulky, and expensive. This paper presents the electrochemical analysis of 6 types of frying oils inclusive of 52 frying timepoints, without sample preparation. This is achieved via impedance spectroscopy to capture sample-specific electrical polarization states. To the best of our knowledge, this is a first-of-its-kind comprehensive study of various types of frying oils, with progressively increasing frying timepoints for each type. The principal component analysis distinguishes the frying timepoints well for all oil types. TPC prediction follows, involving supervised machine learning with sample-wise leave-one-out implementation. The R2 values and mean absolute errors across the test samples measure 0.93-0.97 and 0.43-1.19 respectively. This work serves as a reference for electrochemical analysis of frying oils, with the potential for portable TPC predictors for rapid accurate screening of frying oils.

Nanotunnels within Poly(3,4-ethylenedioxythiophene)-Carbon Nanotube Composite for Highly Sensitive Neural Interfacing.

Neural electrodes are developed for direct communication with neural tissues for theranostics. Although various strategies have been employed to improve performance, creating an intimate electrode-tissue interface with high electrical fidelity remains a great challenge. Here, we report the rational design of a tunnel-like electrode coating comprising poly(3,4-ethylenedioxythiophene) (PEDOT) and carbon nanotubes (CNTs) for highly sensitive neural recording. The coated electrode shows a 50-fold reduction in electrochemical impedance at the biologically relevant frequency of 1 kHz, compared to the bare gold electrode. The incorporation of CNT significantly reinforces the nanotunnel structure and improves coating adhesion by ∼1.5 fold. In vitro primary neuron culture confirms an intimate contact between neurons and the PEDOT-CNT nanotunnel. During acute in vivo nerve recording, the coated electrode enables the capture of high-fidelity neural signals with low susceptibility to electrical noise and reveals the potential for precisely decoding sensory information through mechanical and thermal stimulation. These findings indicate that the PEDOT-CNT nanotunnel composite serves as an active interfacing material for neural electrodes, contributing to neural prosthesis and brain-machine interface.

Implantable neurotechnologies: a review of micro- and nanoelectrodes for neural recording.

Electrodes serve as the first critical interface to the biological organ system. In neuroprosthetic applications, for example, electrodes interface to the tissue for either signal recording or tissue stimulation. In this review, we consider electrodes for recording neural activity. Recording electrodes serve as wiretaps into the neural tissues, providing readouts of electrical activity. These signals give us valuable insights into the organization and functioning of the nervous system. The recording interfaces have also shown promise in aiding treatment of motor and sensory disabilities caused by neurological disorders. Recent advances in fabrication technology have generated wide interest in creating tiny, high-density electrode interfaces for neural tissues. An ideal electrode should be small enough and be able to achieve reliable and conformal integration with the structures of the nervous system. As a result, the existing electrode designs are being shrunk and packed to form small form factor interfaces to tissue. Here, an overview of the historic and state-of-the-art electrode technologies for recording neural activity is presented first with a focus on their development road map. The fact that the dimensions of recording electrode sites are being scaled down from micron to submicron scale to enable dense interfaces is appreciated. The current trends in recording electrode technologies are then reviewed. Current and future considerations in electrode design, including the use of inorganic nanostructures and biologically inspired or biocomapatible materials are discussed, along with an overview of the applications of flexible materials and transistor transduction schemes. Finally, we detail the major technical challenges facing chronic use of reliable recording electrode technology.

Correlation between muscular and nerve signals responsible for hand grasping in non-human primates.

Neuroprosthetic devices that interface with the nervous system to restore functional motor activity offer a viable alternative to nerve regeneration, especially in proximal nerve injuries like brachial plexus injuries where muscle atrophy may set in before nerve re-innervation occurs. Prior studies have used control signals from muscle or cortical activity. However, nerve signals are preferred in many cases since they permit more natural and precise control when compared to muscle activity, and can be accessed with much lower risk than cortical activity. Identification of nerve signals that control the appropriate muscles is essential for the development of such a `bionic link'. Here we examine the correlation between muscle and nerve signals responsible for hand grasping in the M. fascicularis. Simultaneous recordings were performed using a 4-channel thin-film longitudinal intra-fascicular electrode (tf-LIFE) and 9 bipolar endomysial muscle electrodes while the animal performed grasping movements. We were able to identify a high degree of correlation (r > 0.6) between nerve signals from the median nerve and movement-dependent muscle activity from the flexor muscles of the forearm, with a delay that corresponded to 25 m/s nerve conduction velocity. The phase of the flexion could be identified using a wavelet approximation of the ENG. This result confirms this approach for a future neuroprosthetic device for the treatment of peripheral nerve injuries.