Liquid-Liquid Interfacial Nanoarchitectonics

Science in the small world has become a crucial key that has the potential to revolutionize materials technology. This trend is embodied in the postnanotechnology concept of nanoarchitectonics. The goal of nanoarchitectonics is to create bio-like functional structures, in which self-organized and hierarchical structures are working efficiently. Liquid-liquid interface like environments such as cell membrane surface are indispensable for the expression of biological functions through the accumulation and organization of functional materials. From this viewpoint, it is necessary to reconsider the liquid-liquid interface as a medium where nanoarchitectonics can play an active role. In this review, liquid-liquid interfacial nanoarchitectonics is classified by component materials such as organic, inorganic, carbon, and bio, and recent research examples are discussed. Examples discussed in this paper include molecular aggregates, supramolecular polymers, conductive polymers film, crystal-like capsules, block copolymer assemblies, covalent organic framework (COF) films, complex crystals, inorganic nanosheets, colloidosomes, fullerene assemblies, all-carbon π-conjugated graphite nanosheets, carbon nanoskins and fullerphene thin films at liquid-liquid interfaces. Furthermore, at the liquid-liquid interface using perfluorocarbons and aqueous phases, cell differentiation controls are discussed with the self-assembled structure of biomaterials. The significance of liquid-liquid interfacial nanoarchitectonics in the future development of materials will then be discussed.

Analysis of Macronutrients in Soil Using Impedimetric Multisensor Arrays

The need to increase food production to address the world population growth can only be fulfilled with precision agriculture strategies to increase crop yield with minimal expansion of the cultivated area. One example is site-specific fertilization based on accurate monitoring of soil nutrient levels, which can be made more cost-effective using sensors. This study developed an impedimetric multisensor array using ion-selective membranes to analyze soil samples enriched with macronutrients (N, P, and K), which is compared with another array based on layer-by-layer films. The results obtained from both devices are analyzed with multidimensional projection techniques and machine learning methods, where a decision tree model algorithm chooses the calibrations (best frequencies and sensors). The multicalibration space method indicates that both devices effectively distinguished all soil samples tested, with the ion-selective membrane setup presenting a higher sensitivity to K content. These findings pave the way for more environmentally friendly and efficient agricultural practices, facilitating the mapping of cropping areas for precise fertilizer application and optimized crop yield.

Multidimensional calibration spaces in Staphylococcus Aureus detection using chitosan-based genosensors and electronic tongue

Mastitis diagnosis can be made by detecting Staphylococcus aureus (S. aureus), which requires high sensitivity and selectivity. Here, we report on microfluidic genosensors and electronic tongues to detect S. aureus DNA using impedance spectroscopy with data analysis employing visual analytics and machine learning techniques. The genosensors were made with layer-by-layer films containing either 10 bilayers of chitosan/chondroitin sulfate or 8 bilayers of chitosan/sericin functionalized with an active layer of cpDNA S. aureus. The specific interactions leading to hybridization in these genosensors allowed for a low limit of detection of 5.90 × 10-19 mol/L. The electronic tongue had four sensing units made with 6-bilayer chitosan/chondroitin sulfate films, 10-bilayer chitosan/chondroitin sulfate, 8-bilayer chitosan/sericin, and 8-bilayer chitosan/gold nanoparticles modified with sericin. Despite the absence of specific interactions, various concentrations of DNA S. aureus could be distinguished when the impedance data were plotted using a dimensionality reduction technique. Selectivity of S. aureus DNA was confirmed using multidimensional calibration spaces, based on machine learning, with accuracy up to 89 % for the genosensors and 66 % for the electronic tongue. Hence, with these computational methods one may opt for the more expensive genosensors or the simpler and cheaper electronic tongue, depending on the sensitivity level required to diagnose mastitis.

Nanoarchitectonic E-Tongue of Electrospun Zein/Curcumin Carbon Dots for Detecting Staphylococcus aureusin Milk

We report a nanoarchitectonic electronic tongue made with flexible electrodes coated with curcumin carbon dots and zein electrospun nanofibers, which could detect Staphylococcus aureus(S. aureus) in milk using electrical impedance spectroscopy. Electronic tongues are based on the global selectivity concept in which the electrical responses of distinct sensing units are combined to provide a unique pattern, which in this case allowed the detection of S. aureus through non-specific interactions. The electronic tongue used here comprised 3 sensors with electrodes coated with zein nanofibers, carbon dots, and carbon dots with zein nanofibers. The capacitance data obtained with the three sensors were processed with a multidimensional projection technique referred to as interactive document mapping (IDMAP) and analyzed using the machine learning-based concept of multidimensional calibration space (MCS). The concentration of S. aureus could be determined with the sensing units, especially with the one containing zein as the limit of detection was 0.83 CFU/mL (CFU stands for colony-forming unit). This high sensitivity is attributed to molecular-level interactions between the protein zein and C-H groups in S. aureus according to polarization-modulated infrared reflection-absorption spectroscopy (PM-IRRAS) data. Using machine learning and IDMAP, we demonstrated the selectivity of the electronic tongue in distinguishing milk samples from mastitis-infected cows from milk collected from healthy cows, and from milk spiked with possible interferents. Calibration of the electronic tongue can also be reached with the MCS concept employing decision tree algorithms, with an 80.1% accuracy in the diagnosis of mastitis. The low-cost electronic tongue presented here may be exploited in diagnosing mastitis at early stages, with tests performed in the farms without requiring specialized laboratories or personnel.

Molecular Machines and Microrobots: Nanoarchitectonics Developments and On-Water Performances

This review will focus on micromachines and microrobots, which are objects at the micro-level with similar machine functions, as well as nano-level objects such as molecular machines and nanomachines. The paper will initially review recent examples of molecular machines and microrobots that are not limited to interfaces, noting the diversity of their functions. Next, examples of molecular machines and micromachines/micro-robots functioning at the air-water interface will be discussed. The behaviors of molecular machines are influenced significantly by the specific characteristics of the air-water interface. By placing molecular machines at the air-water interface, the scientific horizon and depth of molecular machine research will increase dramatically. On the other hand, for microrobotics, more practical and advanced systems have been reported, such as the development of microrobots and microswimmers for environmental remediations and biomedical applications. The research currently being conducted on the surface of water may provide significant basic knowledge for future practical uses of molecular machines and microrobots.

Materials Discovery With Machine Learning and Knowledge Discovery

Machine learning and other artificial intelligence methods are gaining increasing prominence in chemistry and materials sciences, especially for materials design and discovery, and in data analysis of results generated by sensors and biosensors. In this paper, we present a perspective on this current use of machine learning, and discuss the prospects of the future impact of extending the use of machine learning to encompass knowledge discovery as an essential step towards a new paradigm of machine-generated knowledge. The reasons why results so far have been limited are given with a discussion of the limitations of machine learning in tasks requiring interpretation. Also discussed is the need to adapt the training of students and scientists in chemistry and materials sciences, to better explore the potential of artificial intelligence capabilities.

Biomimetic and Biological Nanoarchitectonics

A post-nanotechnology concept has been assigned to an emerging concept, nanoarchitectonics. Nanoarchitectonics aims to establish a discipline in which functional materials are fabricated from nano-scale components such as atoms, molecules, and nanomaterials using various techniques. Nanoarchitectonics opens ways to form a more unified paradigm by integrating nanotechnology with organic chemistry, supramolecular chemistry, material chemistry, microfabrication technology, and biotechnology. On the other hand, biological systems consist of rational organization of constituent molecules. Their structures have highly asymmetric and hierarchical features that allow for chained functional coordination, signal amplification, and vector-like energy and signal flow. The process of nanoarchitectonics is based on the premise of combining several different processes, which makes it easier to obtain a hierarchical structure. Therefore, nanoarchitectonics is a more suitable methodology for creating highly functional systems based on structural asymmetry and hierarchy like biosystems. The creation of functional materials by nanoarchitectonics is somewhat similar to the creation of functional systems in biological systems. It can be said that the goal of nanoarchitectonics is to create highly functional systems similar to those found in biological systems. This review article summarizes the synthesis of biomimetic and biological molecules and their functional structure formation from various viewpoints, from the molecular level to the cellular level. Several recent examples are arranged and categorized to illustrate such a trend with sections of (i) synthetic nanoarchitectonics for bio-related units, (ii) self-assembly nanoarchitectonics with bio-related units, (iii) nanoarchitectonics with nucleic acids, (iv) nanoarchitectonics with peptides, (v) nanoarchitectonics with proteins, and (vi) bio-related nanoarchitectonics in conjugation with materials.

Machine Learning Analysis for Phenolic Compound Monitoring Using a Mobile Phone-Based ECL Sensor

Machine learning (ML) can be an appropriate approach to overcoming common problems associated with sensors for low-cost, point-of-care diagnostics, such as non-linearity, multidimensionality, sensor-to-sensor variations, presence of anomalies, and ambiguity in key features. This study proposes a novel approach based on ML algorithms (neural nets, Gaussian Process Regression, among others) to model the electrochemiluminescence (ECL) quenching mechanism of the [Ru(bpy)₃]²⁺/TPrA system by phenolic compounds, thus allowing their detection and quantification. The relationships between the concentration of phenolic compounds and their effect on the ECL intensity and current data measured using a mobile phone-based ECL sensor is investigated. The ML regression tasks with a tri-layer neural net using minimally processed time series data showed better or comparable detection performance compared to the performance using extracted key features without extra preprocessing. Combined multimodal characteristics produced an 80% more enhanced performance with multilayer neural net algorithms than a single feature based-regression analysis. The results demonstrated that the ML could provide a robust analysis framework for sensor data with noises and variability. It demonstrates that ML strategies can play a crucial role in chemical or biosensor data analysis, providing a robust model by maximizing all the obtained information and integrating nonlinearity and sensor-to-sensor variations.