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We present an approach to utilize gold nanoparticles embedded within various semiconductor thin films as a possible solution in bringing plasmonic enhancement to photocatalysis in microreactors. In this manner we aim to ensure a solution that is compatible with continuous laminar flow in the device as well as with planar technologies used in microreactor fabrication for seamless integration with the device. We utilize TiO2 , SiO2 and Si materials to optimize optical energy density distributions within the structure and consequently enhance desired carrier generation mechanisms within the metal and the semiconductor. Utilizing thin films instead of core-shell particles offers additional degrees of freedom in terms of material choice as well as multilayer capabilities for any further optimization and increases functionalization of our approach. Furthermore, our solution is compatible with omnidirectional illumination of the microreactor.

Plasmonic biochemical sensors, as a subgroup of optical refractometric sensors, are a topic of rapid research and development due to their ability to detect trace amounts of biochemical agents, even allowing detection of a single molecule. As such, plasmonic sensors find wide applications in forensic engineering, medicine and clinical diagnostics, food processing, environmental protection, and many more. Additionally, the use of plasmonic metametrials is not limited to sensing, and they can be used in various fields paramount to forensic sciences, such as enhanced microscopy and spectroscopy, photodetector enhancement (entire optical range – UV, VIS, and IR light), signal processing, etc.

Polymer-based microelectromechanical systems (MEMS) and devices are a rapidly expanding field with a wide range of applications, from microfluidics including lab-on-a-chip systems, to new sensors and actuators, to flexible and stretchable devices. The use of microfluidic devices and, more broadly, MEMS structures based on polymer materials, has increased tremendously due to their great potential in biomedical, point-of-care, and general healthcare applications. Currently, polymer materials (elastomers, thermoplastics, and thermosetting polymers) are commonly used in the fabrication of microfluidic devices and MEMS structures for their low cost, versatile fabrication methods, biocompatibility, repeatability, and a wide range of (customizable) material characteristics with improved functionality. Polymers have become one of the favored functional materials for microfluidic devices, as they possess excellent mechanical and optical properties. Furthermore, a variety of polymeric materials with unique physical and chemical characteristics are also available. Compared to silicon and glass, polymers are inexpensive, feature a wide variety of material properties for meeting various application requirements of disposable biomedical microfluidic devices and MEMS structures, and have a wide variety of practical applications. In this chapter, different types of polymer materials used in microfluidics and MEMS devices, the properties of polymer materials, and the methods for fabricating polymer-based microfluidic systems and MEMS structures are described. This chapter highlights the most recent developments, future trends, and challenges in the devices’ preparation methods, and applications of polymer-based microfluidic and MEMS devices generally. Special care has been dedicated to biosensing and applications where polymer-based microfluidic and MEMS devices allow for new functionalities: applications based on the use of flexible and stretchable electronic devices, applications

This study discusses the use of a triple material gate (TMG) junctionless tunnel field-effect transistor (JLTFET) as a biosensor to identify different protein molecules. Among the plethora of existing types of biosensors, FET/TFET-based devices are fully compatible with conventional integrated circuits. JLTFETs are preferred over TFETs and JLFETs because of their ease of fabrication and superior biosensing performance. Biomolecules are trapped by cavities etched across the gates. An analytical mathematical model of a TMG asymmetrical hetero-dielectric JLTFET biosensor is derived here for the first time. The TCAD simulator is used to examine the performance of a dielectrically modulated label-free biosensor. The voltage and current sensitivity of the device and the effects of the cavity size, bioanalyte electric charge, fill factor, and location on the performance of the biosensor are also investigated. The relative current sensitivity of the biosensor is found to be about 1013. Besides showing an enhanced sensitivity compared with other FET- and TFET-based biosensors, the device proves itself convenient for low-power applications, thus opening up numerous directions for future research and applications.

The application of artificial intelligence in everyday life is becoming all-pervasive and unavoidable. Within that vast field, a special place belongs to biomimetic/bio-inspired algorithms for multiparameter optimization, which find their use in a large number of areas. Novel methods and advances are being published at an accelerated pace. Because of that, in spite of the fact that there are a lot of surveys and reviews in the field, they quickly become dated. Thus, it is of importance to keep pace with the current developments. In this review, we first consider a possible classification of bio-inspired multiparameter optimization methods because papers dedicated to that area are relatively scarce and often contradictory. We proceed by describing in some detail some more prominent approaches, as well as those most recently published. Finally, we consider the use of biomimetic algorithms in two related wide fields, namely microelectronics (including circuit design optimization) and nanophotonics (including inverse design of structures such as photonic crystals, nanoplasmonic configurations and metamaterials). We attempted to keep this broad survey self-contained so it can be of use not only to scholars in the related fields, but also to all those interested in the latest developments in this attractive area.

New materials are of essential importance for the advancement of nanophotonics and nanoplasmonics. Numerous electromagnetic modes, especially various evanescent surface waves, prove themselves useful in multitudinous practical applications. Here we investigate the use of MXenes as alternative plasmonic materials in freestanding (substrateless) planar nanocomposites that support the existence of Tamm plasmon polaritons (TPP). We use finite element simulations to consider the influence of using MXenes on the propagation and distribution of TPP and the difference in their electromagnetic behavior compared to that of commonly used noble metals. While MXenes allow for somewhat weaker coupling between incident light and TPP, even the thinnest MXene layers practically completely screen the structure behind them. Our diffraction grating-enhanced stacks achieved incident light direction-dependent improvement of the coupling strength and polarization-dependent hybridization of electromagnetic states. MXene ensures improvements in functionality, especially spectral, directional, and polarization selectivity, by imparting rich modal behavior. Importantly, we observed high optical asymmetry of reflectance when illuminating the structures from opposite directions and obtained large high-to-low reflection ratios with a very small number of dielectric layers in the capping 1D photonic crystal. We conclude that MXenes represent a viable alternative for TPP-supporting structures, offering many advantages.

A new set of software tools for the prediction of the higher heating values (HHV) of arbitrarily chosen biomass species is presented. A comparative qualitative and quantitative analysis of 12 algorithms for training artificial neural networks (ANN) which predict the HHV of biomass using the proximate analysis is given. Fixed carbon, volatile matter and ash percentage were utilized as inputs. Each ANN had the same structure but a different training algorithm (BFGS Quasi Newton, Bayesian Regularization, Conjugate Gradient—Powell/Beale Restarts, Fletcher–Powell Conjugate Gradient, Polak–Ribiére Conjugate Gradient, Gradient Descent, Gradient Descent Momentum, Variable Learning Rate Gradient Descent, Levenberg–Marquardt, One Step Secant, Resilient Backpropagation, Scaled Conjugate Gradient). To ensure an extended applicability of our results to a wide range of different biomass species, the data conditioning was based on diverse experimental data gathered from the literature, 447 samples overall. Out of these, 301 datasets were used for the training, validation and testing by MathWorks MATLAB Neural Network Fitting Application and by custom designed codes, and 146 remaining datasets were used for the independent evaluation of all training algorithms. The HHV predictions of the ANN-based fitting functions were thoroughly tested and intercompared, to which purpose we developed a test suite which applies mean squared error, coefficient of the determination, mean Poisson deviance, mean Gamma deviance and Friedman test. The comparative analysis showed that several algorithms resulted in ANN-based fitting functions whose outputs correlated well with measured values of the HHV. All programming codes are freely downloadable.

This paper proposes a monostable nonlinear Piezoelectric Energy Harvester (PEH). The harvester is based on an unconventional exsect-tapered fixed-guided spring design, which introduces nonlinearity into the system due to the bending and stretching of the spring. The physical–mathematical model and finite element simulations were performed to analyze the effects of the stretching-induced nonlinearity on the performance of the energy harvester. The proposed exsect-tapered nonlinear PEH shows a bandwidth and power enhancement of 15.38 and 44.4%, respectively, compared to conventional rectangular nonlinear PEHs. It shows a bandwidth and power enhancement of 11.11 and 26.83%, respectively, compared to a simple, linearly tapered and nonlinear PEH. The exsect-tapered nonlinear PEH improves the power output and operational bandwidth for harvesting low-frequency ambient vibrations.

Nanomembranes are the most widespread building block of life, as they encompass cell and organelle walls. Their synthetic counterparts can be described as freestanding or free-floating structures thinner than 100 nm, down to monatomic/monomolecular thickness and with giant lateral aspect ratios. The structural confinement to quasi-2D sheets causes a multitude of unexpected and often counterintuitive properties. This has resulted in synthetic nanomembranes transiting from a mere scientific curiosity to a position where novel applications are emerging at an ever-accelerating pace. Among wide fields where their use has proven itself most fruitful are nano-optics and nanophotonics. However, the authors are unaware of a review covering the nanomembrane use in these important fields. Here, we present an attempt to survey the state of the art of nanomembranes in nanophotonics, including photonic crystals, plasmonics, metasurfaces, and nanoantennas, with an accent on some advancements that appeared within the last few years. Unlimited by the Nature toolbox, we can utilize a practically infinite number of available materials and methods and reach numerous properties not met in biological membranes. Thus, nanomembranes in nano-optics can be described as real metastructures, exceeding the known materials and opening pathways to a wide variety of novel functionalities.

This work is dedicated to parameter optimization for a self-biased amplifier to be used in preamplifiers for the diagnosis of seizures in neuro-diseases such as epilepsy. For the sake of maximum compactness, which is obligatory for all implantable devices, power is to be supplied by a piezoelectric nanogenerator (PENG). Several meta-heuristic optimization algorithms and an ANN (artificial neural network)-assisted goal attainment method were applied to the circuit, aiming to provide us with the set of optimal design parameters which ensure the minimal overall area of the preamplifier. These parameters are the slew rate, load capacitor, gain–bandwidth product, maximal input voltage, minimal input voltage, input voltage, reference voltage, and dissipation power. The results are re-evaluated and compared in the Cadence 180 nm SCL environment. It has been observed that, among the metaheuristic algorithms, the whale optimization technique reached the best values at low computational cost, decreased complexity, and the highest convergence speed. However, all metaheuristic algorithms were outperformed by the ANN-assisted goal attainment method, which produced a roughly 50% smaller overall area of the preamplifier. All the techniques described here are applicable to the design and optimization of wearable or implantable circuits.

Analytical modelling of adsorption-desorption (AD) processes in micromechanical resonators enables an improved control over operation and modelling of devices based on them. It may be used either to suppress the influence of AD processes (to improve resonators stability) or to enhance it (to improve the response of resonator-based sensors). We analyse random variations of the adsorbed particles number that lead to fluctuations of mass loading on the resonator surface, known as mass loading noise. This phenomenon increases the total frequency noise of the resonator, thus degrading its performance. The novelty is that we consider a resonator with a bimodal affinity surface, exposed to low pressures/concentrations of adsorbate, so that the adsorption-caused adsorbate depletion in the resonator chamber is significant. A novel expression that describes the power spectral density (PSD) of the fluctuations of the number of adatoms is developed, yielding the PSD of mass loading noise. A comparative analysis of the noise models, of which one takes into account adsorbate depletion, while the other does not, leads to the conclusion about the necessity of application of the model derived here. This work can be of practical interest for various classes of micromechanical resonator based devices, including miniature oscillators and highly sensitive sensors.

Recently, biological nanostructures became an important source of inspiration for plasmonics, with many described implementations and proposed applications. Among them are brochosome-inspired plasmonic microstructures—roughly spherical core-shell particles with submicrometer diameters and with indented surfaces. Our intention was to start from the nanoplasmonic point of view and to systematically classify possible alternative forms of brochosome-inspired metal-containing particles producible by the state-of-the-art nanofabrication. A wealth of novel structures arises from this systematization of bioinspired metal-containing nanocomposites. Besides various surface nanoapertures, we consider structures closely related to them in electromagnetic sense like surface nano-protrusions, shell reliefs obtained by nano-sculpting, and various combinations of these. This approach helped us build a new design toolbox for brochosome-inspired structures. Additionally, we used the finite elements method to simulate the optical properties of simple brochosome-inspired structures. We encountered a plethora of advantageous optical traits, including enhanced absorption, antireflective properties, and metamaterial behavior (effective refractive index close to zero or negative). We conclude that the presented approach offers a wealth of traits useful for practical applications. The described research represents our attempt to outline a possible roadmap for further development of bioinspired nanoplasmonic particles and to offer a source of ideas and directions for future research.

Highly sensitive detection of biological analytes by plasmonic sensors is based on analyte adsorption, which modifies the effective refractive index (RI) at the medium-sensor interface. The main issue with such sensors is the non-ideal sensor selectivity (manifested in multiple analyte adsorption), while sample depletion (significant at low analyte concentrations) also causes a deviation of the sensor response from the expected time dependence. Mathematical modelling of sensor response, presented in this work, simultaneously tackles these two issues that may cause erroneous interpretation of measurement results. Two mathematical models that address the problem of limited selectivity are utilized to analyse the sensor response: the linear (pseudo-first order) model, commonly used for interpretation of measurements, and the nonlinear (second order) model that takes into account analyte depletion in the sensor chamber. While the nonlinear model has a virtue of generality, the advantages of the linear model are its simplicity and a well-established procedure for the analysis of experimental results. However, the linear model can exhibit large errors in the case of significant analyte depletion. Until now, the influence of analyte depletion on the linear model fidelity has not been studied for multianalyte adsorption. In this paper, we quantify the difference between the RI changes predicted by the two models of two-analyte competitive adsorption, and establish the conditions that justify the use of the linear model. The validity of these conditions is confirmed by numerically solving the exact nonlinear model equations. The conditions enable making an objective decision whether it is safe to use the linear model for detection of ultralow analyte concentrations, or the nonlinear model must be used in order to avoid false-positive/false-negative detection results and analyte quantification errors. The results are applicable in adsorption-based chemical/biological sensing

This paper presents a study on the design and multiobjective optimization of a bimorph-segmented linearly tapered piezoelectric harvester for low-frequency and multimode vibration energy harvesting. The procedure starts with a significant number of FEM simulations of the structure with different geometric dimensions—length, width, and tapering ratio. The datasets train the artificial neural network (ANN) that provides the fitting function to be modified and used in algorithms for optimization, aiming to achieve minimal resonant frequency and maximal generated power. Levenberg–Marquardt (LM) and scaled conjugate gradient (SCG) methods were used to train the ANN, then the goal attainment method (GAM) and genetic algorithm (GA) were used for optimi-zation. The dominant solution resulted from optimization by the genetic algorithm integrated with the ANN fitting function obtained by the SCG training method. The optimal piezoelectric harvester is 121.3 mm long and 71.56 mm wide and has a taper ratio of 0.7682. It ensures over five times greater output power at frequencies below 200 Hz, which benefits the low frequency of the vibration spectrum. The optimized design can harness the power of higher-resonance modes for multimode applications.

This paper presents the results of the deploying machine learning models in the design and optimization of a unimorph tapered cantilever with proof mass, aimed for piezoelectric energy harvesting. Multiobjective optimization as described in the paper was performed in order to find the optimal dimensions of the structure, its length, its width at the anchor and the ratio between widths at the anchor and at the tip, with respect to the salient parameters for the energy harvesting applications, namely low frequency and high power generated by the structure. The method is applicable for the optimization of the design of more complex MEMS structures aimed for energy harvesting applications.

Surface density of adsorption sites on an adsorbent (including affinity-based sensors) is one of the basic input parameters in modeling of process kinetics in adsorption based devices. Yet, there is no simple expression suitable for fast calculations in current multiscale models. The published experimental data are often application-specific and related to the equilibrium surface density of adsorbate molecules. Based on the known density of adsorbed gas molecules and the surface coverage, both of these in equilibrium, we obtained an equation for the surface density of adsorption sites. We applied our analysis to the case of pristine graphene and thus estimated molecular dynamics of adsorption on it. The monolayer coverage was determined for various pressures and temperatures. The results are verified by comparison with literature data. The results may be applicable to modeling of the surface density of adsorption sites for gas adsorption on other homogeneous crystallographic surfaces. In addition to it, the obtained analytical expressions are suitable for training artificial neural networks determining the surface density of adsorption sites on a graphene surface based on the known binding energy, temperature, mass of adsorbate molecules and their affinity towards graphene. The latter is of interest for multiscale modelling.