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Random sequences and maps are essential for the applications in cryptography and many other fields in Information Technologies. To achieve true randomness, one still needs to refer to natural phenomena, be it physical, chemical or biological. Here we analyse the possibility to apply stochastic physico–chemical dynamics to generate truly random sequences and bitmaps. To this purpose, we utilize simulations of adsorption–desorption (AD) based intrinsic noise in realistic microsystems and nanosystems. We focus on affinity-based chemical or biological sensors, for instance those based on micro or nanocantilevers, as well as those utilizing plasmonic and generally metamaterial phenomena (refractometric nanosensors.) Random numbers in sequences or maps in both types of structures are generated by simulations of intrinsic fluctuations of AD processes. We present three novel AD-simulation based algorithms, two of them for bitstreams and one for dynamic bitmaps. We use the stochastic simulation algorithm developed for modelling of chemical kinetics. We tested the obtained pseudo random numbers by visual analysis and using our custom designed test suite. We have proven the applicability of the proposed method for generation of random sequences and maps. Our results could be used in digital data encryption and communication. They also point out to a possibility to a hardware implementation of a full random number generator that would incorporate the mentioned micro and nanosystems.

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.

Plasmonic metamaterials open a pathway to a multitude of different applications, from ultrasensitive sensors to merging the packaging density of electronics and the speed of photonics in a single all-optical device. A severe limitation to their wider use is high absorption due to the mandatory presence of free electron-containing conductive parts which are lossy by definition. In this contribution we consider an alternative solution in the form of ultrathin silicon films deposited on a silica substrate. The films are patterned as arrays of elongated rhombuses acting as bowtie nanoantennas. We consider the effect of sharp tips and the proximity effect on the electromagnetic field concentration in such low-loss metasurfaces. Our structures have the advantage of very low absorption losses with an order of magnitude field enhancement and the virtue of full compatibility with the standard planar technologies. This makes them convenient for various practical applications which integrate high field concentration with e.g. waveguiding properties, for instance microreactors, labs-on-a-chip, photocatalytic systems and various other Micro-Opto-Electro-Mechanical System (MOEMS) devices integrating optical, microfluidic and other functionalities.

The importance of adsorption-based biochemical/biological sensors in biochemistry and biophysics is paramount. Their temporal response gives information about the presence of a biochemical/biological analyte, its concentration and its interactions with the adsorption sites (which may be an integral part of the surface itself or immobilized functionalizing molecules). Mathematical models of the temporal response taking into account as many relevant effects as possible are essential for obtaining reliable information. We present a novel model taking into account the bimodal affinity of a sensing surface (adsorption occurs on two distinct site types), and the adsorption-caused depletion of the analyte from the sample. We perform qualitative and quantitative analysis of the analyte depletion influence on the bimodal adsorption, and of the influence of the sensing surface inhomogeneity on the sensor temporal response, for different analyte concentrations and different fractions of two types of adsorption sites. Since the presented mathematical model deals with the realistic cases of the sensing surface non-uniformity and the finite amount of analyte present in the sensor reaction chamber, it enables improved accuracy in interpreting the measurement data. Our results are general, i.e. valid for any adsorption sensor (microcantilevers, plasmonics) and for arbitrary sensor dimensions.

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.

We proposed and investigated a family of multifunctional plasmonic biomimetic structures inspired by natural brochosome powders secreted by leafhoppers (insects from Cicadellidae family): spheres with diameters (600–1000) nm, with dielectric or hollow cores and golden shells perforated by subwavelength holes (30–100) nm in radius. We simulated ab initio their optical properties using the finite element method. We found that even in the simplest case (core-shells with only 6 nanoholes) our approach ensured the design of highly efficient omnidirectional ultra-antireflective diffractive powders. The reflectivity of 600 nm diameter perforated spheres did not exceed 2% in (500–600) nm range. We also discovered that planar arrays of our particles exhibited effective optical metamaterial behavior, including ultralow and negative refractive index at near-infrared wavelengths above 1000 nm. This rich optical behavior enhances the multifunctionality of our particles which can serve as antireflective, superhydrophobic and highly porous structures controllable by design. Potential practical impact includes their use in chemical sensing and biosensing, photodetection (antireflection,) photoelectrochemistry, photocatalysis, medical thermoplasmonics, general microoptoelectromechanical systems (MOEMS), etc. Our approach extends the range of possible designs, geometries and materials of brochosome-inspired microparticles. Thus we arrived at a new toolbox for the design of simple, highly customizable and versatile porous plasmonic particles.

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 optimization. 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.

The paper presents the comparative qualitative and quantitative analysis of twelve algorithms for training artificial neural networks (ANN) which predict the higher heating value (HHV) of biomass based on the proximate analysis (fixed carbon, volatile matter, and ash percentage). The twelve networks, with the same structure but different training algorithm, were fed with 318 experimental data triplets from literature for different biomass species and trained with 318 corresponding HHV values used for the supervised learning. Our comparative analysis showed that several algorithms resulted in ANNs generating outputs well correlated with the true measured values of the biomass HHV. Of those, Levenberg-Marquardt algorithm gives the best results in terms of mean squared error calculated on the training set data, while Bayesian regularization gives the best results in terms of regression. When applied to new datasets, unknown to the ANNs trained here, the highest accuracy of the HHV prediction was obtained by Conjugate Gradient, Powell/Beale Restarts training function. It ensured prediction based on the unknown datasets better than Levenberg-Marquardt algorithm. The described approach can be used for predicting the calorific values of different biomass species, including newly proposed ones, as well as for optimizing the HHV for both pure biomass and biomass mixtures.

In order to allow for multiscale modeling of complex systems we focus on various approaches to modeling binary adsorption. We consider multiple methods of modeling the temporal response of general plasmonic sensors. We start from the analytical approach. The kinetics of adsorption and desorption is modeled both as a first order reaction and as a second order reaction. The criteria for their validity and the choice between them in the case of two-component adsorption are established. Due to the nonlinearities of the second order reactions and the lack of their analytical solutions, computer aided modeling is considered next, also in multiple ways: the employment of numerical solvers, fitting of experimental results, the stochastic simulation algorithms and the employment of artificial neural networks (ANN). The examples we present illustrate the advantages and disadvantages of the particular approaches. The goal is to aid the concurrent multiscale modeling of adsorption-based devices. Machine learning in ANN performed here is used to estimate the equilibrium values of adsorbed quantities. The obtained results show that to train an ANN for the estimation of the equilibrium adsorption quantities the Levenberg-Marquardt and the Bayesian regularization algorithms are less efficient than the quasi-Newton BFGS (Broyden-Fletcher-Goldfarb-Shanno) algorithm. This is a post-peer-review, pre-copyedit version of an article (Jakšić, O., Jokić, I., Jakšić, Z. et al. The time response of plasmonic sensors due to binary adsorption: analytical versus numerical modeling) published in Applied Physics A: Materials Science and Processing (Appl. Phys. A 126, 342, 2020) The final authenticated version is available online at: [https://doi.org/10.1007/s00339-020-03524-3] The published version: [http://cer.ihtm.bg.ac.rs/handle/123456789/3494]

In this contribution we propose, design and numerically analyze a plasmonic metamaterial for enhanced optical absorption, based on 100 nm thick titanium carbide (Ti3C2Tx) MXene sheets. The analyzed metamaterial is built as a sandwich with a solid MXene bottom (ground) layer, a lossless dielectric middle layer and an MXene mesh top layer. The unit cell of the periodic top mesh consists of two crossed ultrathin MXene strips, each of them spreading its width in step-like increments towards the middle of the unit cell. This ensures position-variable width of the top surface apertures, resulting in a widening of the bandwidth of spectral dispersion of the scattering parameters of the obtained metamaterial. We utilize the finite element method to simulate the scattering parameters of the MXene-based metamaterial. We apply Drude–Lorentz model to derive our analytical expression for complex permittivity of Ti3C2Tx MXene based on experimental measurements. The described approach is general, since various alternative plasmonic materials can be utilized, including different MXenes, but other materials as well, such as graphene, metals and metal alloys, semiconductors, etc. The approach is applicable to various other nanoplasmonic structures. In this manner the available toolbox for plasmonics is extended and a new degree of design freedom ensured.

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.

Plasmonic metamaterials offer a multitude of different applications. A limitation to their wider use is high absorption. Here we consider an alternative solution in the form of silicon-silica nanocomposites to be used as metasurface-based optical concentrators with decreased losses. To this purpose we design and simulate arrays of bowtie nanoantennas, built in ultrathin silicon films on a silica substrate. In comparison to plasmonic structures, field localization is weaker. On the other hand, our structures have very low absorption losses and a full compatibility with standard planar technologies. This makes them convenient for microreactors, lab-on-a-chip devices and various microfluidic MEMS systems.

The aim of this work was to experimentally explore possibility of gas type and pressure detection using ICTM multipurpose device based on Seebeck effect. For this purpose, experimental procedure was established which allowed testing in the pressure range (2· 102 -2·103) Pa. Thermopile voltage was measured in the atmosphere of argon, carbon dioxide and helium. Experimental results justified to some extent simulation data. It was proven that output voltage can serve both for gas type and gas pressure detection.

In this work we review methods to decrease the optical absorption losses in metamaterials. The practical interest for metamaterials is huge, but the possible applications are severely limited by their high inherent optical absorption in the metal parts. We consider the possibilities to fabricate metamaterial with a decreased metal volume fraction, the application of alternative lower-loss plasmonic materials instead of the customary utilized noble metals, the use of all-dielectric, high refractive index contrast subwavelength nanocomposites. Finally, we dedicate our attention to various methods to optimize the frequency dispersion in metamaterials by changing their geometry and composition in order to reach lower absorption, which includes the use of the hypercrystals. The final goal is to widen the range of different metamaterialbased devices and structures, including those belonging to transformation optics. Maybe the most important among them is the fabrication of a novel generation of alloptical or hybrid optical/electronic integrated circuits that would operate at optical frequencies and at the same time would offer a packaging density and complexity of the contemporary integrated circuits, owing to the strong localization of electromagnetic fields enabled by plasmonics.

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 in complex samples, as well as for the new generation single-element multianalyte plasmonic sensors.

We address noise and stability of adsorption-based sensing of ternary gas mixtures in affinity-based MEMS, NEMS and NOEMS sensors. We investigate mechanisms of such chemical sensing in diverse industrial environments where ternary gas mixtures are of importance. in all existing sensing devices signal fluctuations determine their ultimate performance, and in affinity-based nanodevices the prevailing noise is caused by adsorption and desorption of different species at the sensor surface. We consider analytically and numerically detection of three-component gas mixtures. We present results obtained by applying the conventional method for assessing stability of chemical reactions to three component monolayer adsorption. Gas molecules modeling as spherical particles is suitable for applying any model regarding population dynamics, but taking molecular dimension and orientation into account is more realistic. Noise analysis in time and frequency domain is performed for two different classes of ultrahigh sensitivity adsorption based gas sensors: (nano) plasmonic sensors (refractometric devices) and MEMS/NEMS resonator-based devices.

MXenes are an emerging class of two-dimensional (2D) materials consisting of carbides, nitrides or carbonitrides of early transition metals. Due to the metallic type of conductivity of MXenes and their 2D structure, they are an ideal candidate to replace the conductive materials currently used in plasmonics, especially graphene. However, the use of MXenes for plasmonics and metamaterials is in its embryonal stage and practically all relevant publications appeared in the last few years. In this contribution we first consider some methods of MXenes fabrication. We continue by analyzing the optical and electronic properties of MXenes of interest for plasmonics (especially spectral dispersion of complex relative dielectric permittivity). We proceed by reviewing some reported applications of MXenes in plasmonics, including among others conventionally designed chemical and biological sensors based on surface plasmon resonance (SPR), metasurface-based optical absorbers and MXene-based nanoantennas, as well as some possible applications in nonlinear optics. In addition to that, we propose some novel uses of MXenes in plasmonics, including chemical and biological nanosensors, superabsorbers with a variable widths of the nanoholes, etc. It is our opinion that the applications of MXenes in plasmonics introduced until now are only scratching the surface of a vast bulk of potential practical devices, structures and effects.

Starting from the fact that monocomponent adsorption, whether modeled by Lagergren or nonlinear Riccati equation, does not sustain oscillations, we speculate about the nature of multiple steady state states in multicomponent adsorption with second-order kinetics and about the possibility that multicomponent adsorption might exhibit oscillating behavior, in order to provide a tool for better discerning possible oscillations from inevitable fluctuations in experimental results or a tool for a better control of adsorption process far from equilibrium. We perform an analysis of stability of binary adsorption with second-order kinetics in multiple ways. We address perturbations around the steady state analytically, first in a classical way, then by introducing Langevin forces and analyzing the reaction flux and cross-correlations, then by applying the stochastic chemical master equation approach, and finally, numerically, by using stochastic simulation algorithms. Our results show that stationary states in this model are stable nodes. Hence, experimental results with purported oscillations in response should be addressed from the point of view of fluctuations and noise analysis.

Many micro(nano)optoelectromechanical systems (MOEMS, NOEMS) require optical (generally, electromagnetic) field localization and concentration. These include for instance photocatalytic microreactors and labs on a chip, where it is necessary to localize optical energy into a fluidic channel. Other examples are chemical and biological sensors. Plasmonics on the other hand ensures field localization down to subwavelength volumes where evanescent fields can be tailored to the shape of minuscule channels in MOEMS and NOEMS. In this work we present a possible approach to the enhancement of optical fields in MOEMS and NOEMS systems where a linear array of plasmonic bowtie structures is used to concentrate the optical field into a dielectric channel. We perform our numerical simulations using the finite element method to analyze field distributions that can be achieved by the use of the bowtie antenna and the possibility to tailor these fields. We also analyze the influence of the shape of the coupled tips of bowties to the field distribution and frequency dispersion. We conclude that arrays of plasmonic bowties could be a promising candidate for optically assisted micro and nanofluidics.

An important class of electromagnetic metamaterials are aperture-based metasurfaces. Examples include extraordinary optical transmission arrays and double fishnets with negative refractive index. We analyze a generalization of such metamaterials where a simple aperture is now replaced by a compound object formed by superposition of two or more primitive objects (e.g., rectangles, circles, and ellipses). Thus obtained “super unit cell” shows far richer behavior than the subobjects that comprise it. We show that nonlocalities introduced by overlapping simple subobjects can be used to produce large deviations of spectral dispersion even for small additive modifications of the basic geometry. Technologically, some super cells may be fabricated by simple spatial shifting of the existing photolithographic masks. In our investigation we applied analytical calculations andab initiofinite element modeling to prove the possibility to tailor the dispersion including resonances for plasmonic nanocomposites by adjusting the local geometry and exploiting localized interactions at a subwavelength level. Any desired form could be defined using simple primitive objects, making the situation a geometrical analog of the case of series expansion of a function. Thus an additional degree of tunability of metamaterials is obtained. The obtained designer structures can be applied in different fields like waveguiding and sensing.

There is a growing demand for inexpensive and efficient gas sensors which have important applications in various areas including industrial safety and emission monitoring in coal fired and lignite based thermal power plants. Various air polluting and hazardous gases are emitted from power plants (Hydrogen (H 2 ), Nitrogen Dioxide (NO 2 ), Chlorine (Cl 2 ), Carbon Dioxide (CO 2 ), Sulfur Dioxide (SO 2 ), etc). The main goal of this work is to study suitability of use of thermopile based MEMS sensors in systems for gas emission monitoring in thermal power plants using self-developed 1D analytical model.

This paper investigates multicomponent gas adsorption at the active surface of plasmonic chemical sensors and shows that there are situations where transients in a single sensor element can be used for simultaneous detection of different gases in multicomponent mixtures. A general master equation set is provided, describing multicomponent adsorption. Analytical expressions for sorption rates are derived and high-accuracy simplified models are proposed. Expressions for adsorption rate constants and rates and for number of binding sites are proposed. The derived analytical model takes into account the adsorbate molecule size, distribution of binding sites as determined by the crystallographic structure of the sensor surface and multi-site adsorption. The model allows for the calculation and optimization of deterministic behavior of the system. It is shown that trace amounts of target gas species can be made detectable by adding controlled amounts of known carrier gas. Besides being applicable in plasmonic sensor design and optimization, the obtained results may be of importance in situations where fast and low-cost detection of trace amounts of gases is needed, including natural gas leakage in residential heating, radon outgassing in dwellings, environmental protection, homeland defense and hazardous materials management, greenhouse footprint investigations, etc. This is the peer-reviewed version of the article: Jaksic, O.M., Randjelovic, D., Jaksic, Z.S., Cupic, Z.D., Kolar-Anic, L.Z., Plasmonic Sensors in Multi-Analyte environment: rate constants and transient analysis, Chemical Engineering Research and Design (2013), [http://dx.doi.org/10.1016/j.cherd.2013.06.033] [http://cer.ihtm.bg.ac.rs/handle/123456789/1543]

The role of chemical sensors in modern process industry cannot be overestimated, since they are encountered at different stages of process control, helping maintain product quality and at the same time allowing to keep an eye on environmental issues and pollution problems. Numerous device families are currently in use, where optical chemical sensors are probably the fastest, the simplest and the most sensitive. Affinity sensors represent a class of these devices based on adsorption of chemical analytes. The prime example are plasmonic sensors, which are label-free, ultra sensitive and ultra fast. Literature usually defines only the ideal sensitivity of such devices and their performance is taken as granted. In reality there are many parasitic processes that decrease their performance and introduce significant measurement uncertainties. Noise and fluctuations in such sensors can be generated by both extrinsic or intrinsic sources and a significant part of them is a consequence of fundamental processes that at the same time ensure the very function of the devices. The main intrinsic mechanisms include adsorption-desorption, optical flicker and thermal noise, while the level of the extrinsic noise is related with the quality of the interrogating beam source and the photo detector. In this contribution we offer a possible systematization of different sources of noise in refractometric chemical sensors and analyze the influence of such fluctuations to the ultimate device performance.

The analysis of adsorption kinetics is given, assuming no dissociative adsorption takes place and no multiple binding sites exist. The phenomenological nonlinear model and the simple linear model are considered from the point of view of surface plasmon resonance (SPR) chemical sensors. A simple method for analytical calculation of the necessary rate constants is proposed. A comparative analysis of the described models is performed. Two conditions that affect the validity of the linear model are found. First one, referring to the ratio between the overall number of particles and the number of binding sites, and the second one, that guarantees the validity of linear model in much broader spectrum of practical situations, referring to the ratio of rate constants. A method for avoiding the accumulation of numerical errors in calculus is also proposed. The presented approach is general and can be used in various applications, including other types of chemical sensors, membranes for filtering, different catalytic systems, resonant micro or nano-electro-mechanical structures.

In adsorption-based chemical and biological refractometric sensors the dependence of the refractive index of the sensing area on the number of adsorbed particles is used for detection and quantification of analytes. We perform stochastic analysis of equilibrium fluctuations of the adsorbed particles number for monolayer adsorption using a nonlinear second-order model. We derive an analytical expression for the power spectral density (PSD) of the refractive index change fluctuations. Our theory is applicable to sensors operating in closed volume systems with spatially uniform analyte concentration. Our analysis of fluctuations in a benzene sensor shows a significant difference between the PSD according to the derived expression and the PSD obtained by the linear adsorption model, especially for very low amounts of analyte. The developed theory is valid in a wider range of pressures and temperatures. Since signal fluctuations determine the detection limit, the presented theoretical model is applicable as a tool for the design, optimization and characterization of practical adsorption-based refractometric sensors.

We consider the enhancement of the specific detectivity of semiconductor infrared detectors utilizing a redshifted plasmonic light concentrator. To this purpose submicrometer-sized doped transparent conductive oxide particles with different shapes were used, embedded within high dielectric permittivity medium. The whole structure was topped with a graded antireflective layer. Localized surface plasmon resonance ensures high field localization around the plasmonic particles and directly beneath them, which translates into high density of optical energy within the detector active region. We investigate the possibilities to tune the particle shape and size in order to maximize the photodetector optical energy input, i.e. to maximize the detector external quantum efficiency. We perform ab initio simulation of the optical response utilizing the finite element method, starting with spherical particles and then extending this to include spheroids and sub-micrometric plates. As an illustrative example, we analyze a mercury cadmium telluride infrared detector with an ultrathin active epitaxial active layer. An increase in external quantum efficiency is obtained for non-spherical particles compared to the spherical ones. This can be used to offset a decrease in the optical path/internal quantum efficiency. Plasmonic localization is convenient for this since optical energy density is strongly localized close to the illuminated surface, thus ensuring a large decrease of the total detector volume and therefore suppressing the generation-recombination noise levels which are proportional to the device volume. Our results show that it is possible by this approach to realize an uncooled semiconductor detector for mid-wavelength infrared range with its specific detectivity strongly increased compared to the conventional devices.

Nanoplasmonic devices are among the most sensitive chemical sensors, with sensitivities reaching the single-molecule level. An especially convenient class of such sensors is that based on metasurfaces with subwavelength nanoholes, examples being extraordinary optical transmission arrays and double fishnet structures. Such structures ensure operation both in transmission and reflection mode and ensure high sensitivities and excellent coupling with external readout. In this paper we consider the possibility to tailor the response of aperture-based sensor structures by modifying the geometry of nanoholes at the deep subwavelength level through ensuring controlled use of nonlocal effects. We investigate the case where nonlocality is achieved by modifying the basic metamaterial fishnet structure (a metal-dielectric-metal sandwich with rectangular openings) by superposing additional subwavelength patterns, ensuring the appearance of new optical modes. The obtained unit cell superstructure will have multiple tailorable spectral peaks that will increase the selectivity at different wavelengths. The finite elements method was used for simulations of the proposed structures. As an example, we applied our results to the case of a benzene sensor, showing that its spectral properties and selectivity can be tuned by modifying geometry at a deep subwavelength scale. The obtained custom-designed spectral selectivity is convenient for multianalyte chemical sensing using a single structure.

We investigated intrinsic noise in plasmonic sensors caused by adsorption and desorption of gaseous analytes on the sensor surface. We analyzed a general situation when there is a larger number of different analyte species. We applied our model to calculate various analyte mixtures, including some environmental pollutants, toxic and dangerous substances. The spectral density of mean square refractive index fluctuations follows a dependence similar to that of generation-recombination noise in photodetectors, flat at lower frequencies and sharply decreasing at higher. Some of the calculated noise levels are well within the detection range of conventional surface plasmon resonance sensors. An AD noise peak is observed in temperature dependence of mean square refractive index fluctuations, thus sensor operating temperature may be optimized to obtain larger signal to noise ratio. A significant property of AD noise is its rise with the decreasing plasmon sensor area, which means that it will be even more pronounced in modern nanoplasmonic devices. Our consideration is valid both for conventional surface plasmon resonance devices and for general nanoplasmonic devices.

In this paper the theory of the adsorption and desorption process of an arbitrary number of gases on the sensor surface is presented. It is assumed that the sorption follows the Langmuir isotherm. The exact expressions are derived for the power spectral density of the fluctuations of the number of adsorbed particles for each gas from the mixture, as well as for the total adsorbed mass fluctuation, using the analytical Langevin approach. Since these fluctuations cause adsorption–desorption (AD) noise, the presented theory enables determination of the limiting performance of various micro/nanoelectromechanical devices (micro/nanocantilever sensors and oscillators), resistive gas sensors and chemical and biochemical sensors based on surface plasmon resonance effect, and optimization of their parameters. This model is also applicable for the theoretical study of adsorption–desorption processes in general. We used it for investigation of the possibility of identification of gases in the mixture, based on the power spectral density of the adsorbed mass fluctuations. We also considered the possibility of the lowering the adsorbed mass fluctuations (i.e. AD noise level) by adding a certain amount of a gas to the mixture. As an example of the application of the presented theory, we calculate the fluctuations of the mass adsorbed on the surface of the silicon micro/nanocantilever sensor surrounded by the atmosphere of two and three gases.

We analysed some peculiarities of electromagnetic metamaterials convenient for plasmon-based chemical sensing with enhanced sensitivity. Owing to the vastly extended range of possible values of refractive index, a higher degree of design freedom is enabled in comparison to conventional surface plasmon sensors. After considering some possible approaches to use metamaterials as either surface or bulk adsorbents, we analysed the ultimate performance of such sensing devices which are limited by refractive index fluctuations caused by adsorption and desorption processes. We utilized an approach based on a parallel between the adsorption–desorption mechanism and the carrier generation–recombination in semiconductors. As a special case, we considered structures for the detection of a single gas and used our approach to calculate the spectral density of refractive index fluctuations. We believe that our analysis may be of importance in different practical fields, including e.g. environmental sensing, homeland security and biosensing.

This work presents an analytical model dedicated to study of the transient response of multipurpose MEMS devices based on thermopile sensors. In general, thermopile sensors response depends on ambient temperature, thermal conductivity of the gas inside the housing and the pressure of the gas. The presented model takes into account all these parameters. This model was sucessfully implemented for the study of transient behaviour of our multifunctional sensors with p+Si/Al thermocouples and a bulk micromachined bilayered membrane. Simulations were performed for different gases of interest and conclusions were deduced regarding the influence of relevant parameters on the thermal time constant. This analytical approach is general and flexible enough to be implemented for analysis of the transient behaviour of thermopile-based sensors when used for different applications.

We investigated the influence of nanoscale technological imperfections to the macroscopic behavior of ordered composite structures for photonic applications. A connection between the deviations from the designed geometry on nanoscopic level and macroscopic behavior was established using amplitude and frequency noise as the primary figures of merit. We calculated the influence of nanoscale deviations by transfer matrix technique, while surface roughness was taken into account by using the effective medium theory. As an example we considered our nonuniform silicon/silica 1D photonic bandgap structures fabricated by rf sputtering, which contained defect layers with spatially varying thickness. The cross-sections and surface morphology of the samples were analyzed by scanning electron microscopy and atomic force microscopy while spectral transmission was measured by FTIR spectrometry. It was established that thickness deviations of 5-7nm caused scattering losses of 20-40%, while lattice parameter change of 70nm resulted in spectral transmission shift of about 70nm. The approach is applicable to other mesoscopic and subwavelength structures, including those with higher dimensional (2D and 3D) geometries and could be used as a means for fast assessment of optical nanodevice fabrication systems.

Far infrared reflectivity spectra of single crystal PbTe samples doped with Ni (PbTe(Ni)) were measured in the temperature range between 10 K and 300 K. The reflectivity spectra were numerically analyzed using the Kramers Kronig method and a fitting procedure based on the plasmon–phonon interaction model, and the optical parameters were determined. The Hall parameters were measured at 300 K and 77 K and the obtained results were compared with the optical data.

This work is dedicated to modeling of adsorption processes in plasmonic sensors. We give a generalization of the Lagergren first order reaction kinetic model to the situations where adsorption takes place in multianalyte environment and every analyte obeys one of the following two rules: either it has a small equilibrium constant or its initial number of particles greatly exceeds the number of corresponding binding sites on the surface. Our special concern is the influence of propagating numerical errors and peculiarities of mathematical software used to obtain the results. Complete derivations and solutions are given for an example of the temporal response of an adsorption-based plasmonic sensor in a bi- and three-analyte gaseous environment.

In this paper we propose a novel operating principle for thermal vacuum sensor. The sensor considered in this work consists of thermopiles and thermally isolating membrane structure. The operating principle of such sensor would be based on monitoring thermal time constant dependence on pressure. In order to get the most prominent effect, sensor fabricated on SOI wafer is studied. Simulation was performed using 1D analytical model. It is shown that sensors performance could be improved by increasing the number of thermocouples. Apart from that it is demonstrated that performance of such sensor depends also on gas present in the housing so the same device could be also used as a gas type detector.

a b s t r a c t The basic parameters of a sensor element defining its ultimate performance are sensitivity and intrinsic noise. In plasmonic gas sensors both are determined by refractive index changes due to adsorption and desorption (a-d) of target analyte particles to the sensor active area. In this paper we present a general model that can be simultaneously used to determine sensitivity and intrinsic noise of a plasmonic sensor both during transients and in steady-state and is valid for multi-analyte environments. The model util- izes the conventional probabilistic approach. It is derived without any assumptions about the stochastic nature of the fundamental (a-d) process. It reveals how all stochastic properties of the processes with (pseudo) first order kinetics with the initial number of particles equal to zero can be fully determined from the deterministic solution, without any previous stochastic analysis. Based on the proposed model it is possible to establish the optimum moment for readout when fluctuations are minimal. Transients last longer and fluctuations are lower at lower temperatures. The insight into the transient dynamics opens the possibility to use a single element sensor for multiple analyte sensing. Another result is that a-d noise is higher for smaller adsorption areas, which may be important for micro and nanosystems generally, since each of them has to be kept immersed in some kind of environment and thus be subject to contamination by adsorption that can significantly influence their behavior. Besides being applicable for plasmonic sensors of trace amounts of gases and other nanoplasmonic devices used in sensing, the model is applicable for other adsorption-based sensors, as well as for the investigations of stochastic phenomena in micro and nanostructures.

The investigation represented here is focused on fundamental intrinsic fluctuations caused by the adsorption and desorption (a–d) of surrounding particles on the active surface in plasmonic metamaterial devices. The variance and relative fluctuations in adsorption–desorption dynamics are studied based on two different kinetic models (pseudo-first order kinetics and second order kinetics). The limits of the applicability of the pseudo-first order model are determined, thus ensuring the use of the vast mathematical heritage developed for linear systems in calculation of a–d noise, while at the same time speeding up and simplifying calculations. An approach is proposed to assess adsorption in a metamaterial sensor using an effective parameter dependent on the properties of the metamaterial unit cell.

This article's focus is on the numerical estimation of the overall instability of microelectromechanical-system-based (MEMS) resonators, caused by intrinsic noise mechanisms that are different in nature (electrical, mechanical or chemical). Heterogeneous intrinsic noise sources in MEMS resonators that have been addressed here are Johnson–Nyquist noise, 1/f noise, noise caused by temperature fluctuations and adsorptiondesorption induced noise. Their models are given first (based on analytical modeling or based on empirical expressions with experimentally obtained parameters). Then it is shown how each one contributes to the phase noise, a unique figure of merit of resonators instability. Material dependent constants  and knee position in noise spectrum, needed for empirical formulae referring to 1/f noise, have been obtained experimentally, by measurements of noise of MEMS components produced in the Centre of Microelectronic Technologies of the Institute of Chemistry, Technology and Metallurgy in Belgrade. According to these measurements,  varies in the range from 0.776.10-4 to 2.26.10-4 and cut off frequency for 1/f noise varies from 147 Hz to 1 kHz. The determined values are then used for the modeling of micro-resonator phase noise with electrical origin and overall phase noise of a micro-resonator. Numerical example for calculation of overall phase noise is given for a micro-cantilever, produced by the same technology as measured components. The outlined noise analysis can be easily extended and applied to noise analysis of MEMS resonator of an arbitrary shape.