A Multisensor Device Intended as an IoT Element for Indoor Environment Monitoring.

This work presents a multisensor device which is intended as an element of IoT for indoor environment (IE) monitoring. It is a portable, small-size, lightweight, energy-efficient direct-reading instrument. The device has an innovative design and construction. It offers real-time measurements of a wide spectrum of physical and chemical quantities (light intensity, temperature, relative humidity, pressure, CO2 concentration, content of volatile organic compounds including formaldehyde, NO2, and particulate matter), data storage (microSD; server as an option), transmission (WiFi; GSM and Ethernet as options), and visualization (smartphone application; PC as an option). Commercial low-cost sensors were utilized, which have been arranged in the individual sensing modules. In the case of gas sensors, dynamic exposure was chosen to ensure a minimum response time. The MQTT protocol was applied for data transmission and communication with other devices, as well as with the user. The multisensor device can collect huge amounts of data about the indoor environment to provide the respective information to the IoT. The device can be configured to control actuators of various auxiliary devices and equipment including external systems used for ventilation, heating, and air conditioning. The prototype is fully operational. The exemplary results of IE monitoring were shown.

Aerosol penetration study for FFP2 half masks regarding protection against diesel particles in underground mines.

Filtering facepieces (FFP), mainly class FFP2 particle half masks (EN 149:2001#x02009;+ A1:2009), are commonly used in European mines to protect workers from respirable dust, especially from particulate matter (PM) with a diameter of 4 µm or less (PM4). The aerosol associated with diesel exhaust (DE) is dominated by submicrometer particles (with a diameter of less than 1 µm) and nanoparticles (size in the range between 10 and 500 nm). In the European Union (EU), the occupational exposure level (OEL) for DE has been defined in terms of elemental carbon (EC) concentration. Based on measurements in underground mines, on average, 60% of EC associated with PM4 was contained in PM with a diameter of 1 µm or less (PM1). Particle number size distribution (PNSD) of PM1 showed that the most numerous were particles in the size range of 20 to 300 nm. Four popular types of certified FFP2 half masks were tested for penetration. Brand new and thermally conditioned masks of each type were included in the study. NaCl aerosol in the particle size range of 7 to 270 nm was used for tests. Filtration efficiencies of 98.5% (median) or higher were achieved. Aerosol penetration was a function of particle size. Maximum penetration was observed between 20 and 60 nm, depending on the type of mask. During filtration, aerosol characteristics changed. Nanoparticles ranging in size from 7 to about 60 nm were removed to a very limited extent. The change was more noticeable for brand-new masks compared to the thermally conditioned ones. Usually, aerosol penetration through thermally conditioned masks was lower and more consistent. It was confirmed that the half masks of the FFP2 class are capable of filtering submicrometer aerosol in particle size range 7 to 270 nm with an efficiency exceeding 96% and can contribute to achieving compliance with the OEL for DE in the mining sector.

Co-Dependency of IAQ in Functionally Different Zones of Open-Kitchen Restaurants Based on Sensor Measurements Explored via Mutual Information Analysis.

High-quality indoor air is essential in open-kitchen restaurants for ensuring a healthy workplace and comfortable conditions for visitors. In this study, indoor air quality interdependence between the kitchen and the dining zones in open-kitchen restaurants was analyzed. The method was based on measurements of selected air parameters using a sensor technique and mutual information (MI) analysis. A long-term approach (based on a several-hour time series) and a short-term approach (based on a several-minute time series) were applied. This study involved four open-kitchen restaurants. The indoor conditions were represented by the temperature, relative humidity, CO2 concentration, and content of the total volatile organic compounds (TVOC) in the air. The MI analyses showed that the long-term co-dependence of the indoor conditions between the kitchen and the dining zones was smaller during business hours (MI = 0.12 ÷ 0.40) compared to night hours (MI = 0.24 ÷ 0.58). The ranking of the long-term MI values for the individual air parameters was MICO2 (0.34) ≅ MIT (0.34) > MIRH (0.28) > MITVOC (0.23). The short-term interdependencies were smaller during night hours (median MI = 0.01 ÷ 0.56) compared to business hours (MI = 0.23 ÷ 0.61). Additionally, the short-term MI was subject to high temporal variability. The ranking of the short-term MI values for the individual air parameters was MICO2 (0.48) > MIT (0.46) > MIRH (0.37) > MITVOC (0.26). Due to the weak and highly variable co-dependence of the air parameters between the kitchen and dining areas, multi-zone monitoring of air parameters with an emphasis on TVOC measurements is recommended to ensure proper indoor conditions in open-kitchen restaurants. The presented approach may be applied to design indoor air quality monitoring and ventilation systems not only in open-kitchen restaurants but also in other interiors with functionally different zones.

The Gas Sensing Drone with the Lowered and Lifted Measurement Platform.

A serious factor that limits the environmental applications of drones is the disturbance of the air pollution concentration field by the drone propulsion system. This work presents a gas-sensing drone offering measurements that are unaffected by this phenomenon. The novel development was based on the idea that, during measurements, the sensing device should be spatially separated from a zone influenced by the drone's rotors. To attain this goal, special equipment was designed that allows one to undock and lower the sensing device for measurement, lift it and dock for flight. The field experiments demonstrated the full functionality of the developed system and its superiority compared to a sensing platform mounted at the bottom of the drone. Higher measurement sensitivity and resolution were attained by lowering the sensing platform to the measurement point. This solution minimizes the rotor flow effect, ground effect, and pollution concentration field flattening. The test in real conditions confirmed that the designed construction assures drone stability. The presented technology may be an important step in developing effective mobile measurement tools that allow one to reach poorly accessible or dangerous places and perform measurements at a low cost and with high efficiency.

The Detection of Activities Occurring Inside Quick Service Restaurants That Influence Air Quality.

Our attention was focused on the identification of activities affecting air quality, which occur in quick-service restaurants (QSR). The work was based on a measurement study of selected kebab stores in the Polish city of Wroclaw. It demonstrated that activities taking place in kebabs altered air quality. The associated changes in air parameters such as temperature, relative humidity, CO2 concentration, and the content of volatile organic compounds could be detected by utilizing a simple, multi-sensor device. In the measurement data, there were identified multidimensional patterns, which proved to be specific for the following categories of activities: Night Hours, Outlet Preparation, Food Preparation, Operation Hours, and Cleaning. Their occurrence was recognized by pattern recognition methods with a true positive rate greater than 99%. We demonstrated that the recognition may be based on measurements performed in various locations within the kebab store. Although patterns of the individual categories of activities largely varied between kebab stores, a similar performance of recognition was achieved for all restaurants. The obtained results entitled us to conclude that it is possible to detect activities of QSR, which influenced air quality, with the application of sensor technique and pattern recognition. The proposed approach may be applied to this type of object in general.

Developments in Synthesis and Potential Electronic and Magnetic Applications of Pristine and Doped Graphynes.

Doping and its consequences on the electronic features, optoelectronic features, and magnetism of graphynes (GYs) are reviewed in this work. First, synthetic strategies that consider numerous chemically and dimensionally different structures are discussed. Simultaneous or subsequent doping with heteroatoms, controlling dimensions, applying strain, and applying external electric fields can serve as effective ways to modulate the band structure of these new sp2/sp allotropes of carbon. The fundamental band gap is crucially dependent on morphology, with low dimensional GYs displaying a broader band gap than their bulk counterparts. Accurately chosen precursors and synthesis conditions ensure complete control of the morphological, electronic, and physicochemical properties of resulting GY sheets as well as the distribution of dopants deposited on GY surfaces. The uniform and quantitative inclusion of non-metallic (B, Cl, N, O, or P) and metallic (Fe, Co, or Ni) elements into graphyne derivatives were theoretically and experimentally studied, which improved their electronic and magnetic properties as row systems or in heterojunction. The effect of heteroatoms associated with metallic impurities on the magnetic properties of GYs was investigated. Finally, the flexibility of doped GYs' electronic and magnetic features recommends them for new electronic and optoelectronic applications.

Beehive Air Sampling and Sensing Device Operation in Apicultural Applications-Methodological and Technical Aspects.

The basis of effective beekeeping is the information about the state of the bee colony. A rich source of respective information is beehive air. This source may be explored by applying gas sensing. It allows for classifying bee colony states based on beehive air measurements. In this work, we discussed the essential aspects of beehive air sampling and sensing device operation in apicultural applications. They are the sampling method (diffusive vs. dynamic, temporal aspects), sampling system (sample probe, sampling point selection, sample conditioning unit and sample delivery system) and device operation mode ('exposure-cleaning' operation). It was demonstrated how factors associated with the beehive, bee colony and ambient environment define prerequisites for these elements of the measuring instrument. These requirements have to be respected in order to assure high accuracy of measurement and high-quality information. The presented results are primarily based on the field measurement study performed in summer 2020, in three apiaries, in various meteorological conditions. Two exemplars of a prototype gas sensing device were used. These sensor devices were constructed according to our original concept.

Perspectives on Tannins.

Tannins are a family of versatile, natural phenolic biomolecules whose main role is to protect plants against insects and fungi [...].

The Effectiveness of Varroa destructor Infestation Classification Using an E-Nose Depending on the Time of Day.

Honey bees are subject to a number of stressors. In recent years, there has been a worldwide decline in the population of these insects. Losses raise a serious concern, because bees have an indispensable role in the food supply of humankind. This work is focused on the method of assessment of honey bee colony infestation by Varroa destructor. The approach allows to detect several categories of infestation: "Low", "Medium" and "High". The method of detection consists of two components: (1) the measurements of beehive air using a gas sensor array and (2) classification, which is based on the measurement data. In this work, we indicate the sensitivity of the bee colony infestation assessment to the timing of measurement data collection. It was observed that the semiconductor gas sensor responses to the atmosphere of a defined beehive, collected during 24 h, displayed temporal variation. We demonstrated that the success rate of the bee colony infestation assessment also altered depending on the time of day when the gas sensor array measurement was done. Moreover, it was found that different times of day were the most favorable to detect the particular infestation category. This result could indicate that the representation of the disease in the beehive air may be confounded during the day, due to some interferences. More studies are needed to explain this fact and determine the best measurement periods. The problem addressed in this work is very important for scheduling the beekeeping practices aimed at Varroa destructor infestation assessment, using the proposed method.

Detecting varroosis using a gas sensor system as a way to face the environmental threat.

Colony Collapse Disorder (CCD) is an environmental threat on a global scale due to the irreplaceable role of bees in crop pollination. Varroa destructor (V.d.), a parasite that attacks honeybee colonies, is one of the primary causes of honey bee population decline and the most serious threat to the beekeeping sector. This work demonstrates the possibility of quantitatively determining bee colony infestation by V.d. using gas sensing. The results are based on analysing the experimental data acquired for eighteen bee colonies in field conditions. Their infestation rate was in the 0 to 24.76% range. The experimental data consisted of measurements of beehive air with a semiconductor gas sensor array and the results of bee colony V.d. infestation assessment using a flotation method. The two kinds of data were collected in parallel. Partial Least Square regression was applied to identify the relationship between the highly multivariate measurement data provided by the gas sensor array and the V.d. infestation rate. The quality of the developed quantitative models was very high, as demonstrated by the coefficient of determination exceeding R2 = 0.99. Moreover, the prediction error was <0.6% for V.d. infestation rate predictions based on the measurement data that was unknown to the model. The presented work has considerable novelty. To our knowledge, the ability to determine the V.d. infestation rate of bee colony quantitatively based on beehive air measurements using a semiconductor gas sensor array has not been previously demonstrated.

Toward the synthesis, fluorination and application of N-graphyne.

The discovery of properties and applications of unknown materials is one of the hottest research areas in materials science. In this work, we navigate a route towards these goals by the development of a new type of graphyne nanostructure. It is synthesised by a Sonogashira cross-coupling reaction of 1,3,5-triethynylbenzene with cyanuric chloride resulting in an extended carbon-based material called TCC. Also, we modify the obtained TCC via fluorination using XeF2 at various concentrations to investigate the effect of fluorination on the triple bonds and the conjugated structure of graphyne. In this study, we put special emphasis on the determination of the impact of the fluorine content and the type of CF functionalities on the morphology, chemical and electronic structure, biocompatibility, electrical conductivity and possible applicability as anode materials for Li-ion batteries. The obtained results indicate that the character of C-F bonds influences the final properties of fluorinated materials. The polar C-F bonds are preferable for cell proliferation while CF2 groups are most suitable for battery devices, however, the appearance of PTFE-like units may have a negative impact on battery specific capacitance as well as on cell viability.

Gas Sensor Array and Classifiers as a Means of Varroosis Detection.

The study focused on a method of detection for bee colony infestation with the Varroa destructor mite, based on the measurements of the chemical properties of beehive air. The efficient detection of varroosis was demonstrated. This method of detection is based on a semiconductor gas sensor array and classification module. The efficiency of detection was characterized by the true positive rate (TPR) and true negative rate (TNR). Several factors influencing the performance of the method were determined. They were: (1) the number and kind of sensors, (2) the classifier, (3) the group of bee colonies, and (4) the balance of the classification data set. Gas sensor array outperformed single sensors. It should include at least four sensors. Better results of detection were attained with a support vector machine (SVM) as compared with the k-nearest neighbors (k-NN) algorithm. The selection of bee colonies was important. TPR and TNR differed by several percent for the two examined groups of colonies. The balance of the classification data was crucial. The average classification results were, for the balanced data set: TPR = 0.93 and TNR = 0.95, and for the imbalanced data set: TP = 0.95 and FP = 0.53. The selection of bee colonies and the balance of classification data set have to be controlled in order to attain high performance of the proposed detection method.

Structure and Electromagnetic Properties of Cellular Glassy Carbon Monoliths with Controlled Cell Size.

Electromagnetic shielding is a topic of high importance for which lightweight materials are highly sought. Porous carbon materials can meet this goal, but their structure needs to be controlled as much as possible. In this work, cellular carbon monoliths of well-defined porosity and cell size were prepared by a template method, using sacrificial paraffin spheres as the porogen and resorcinol-formaldehyde (RF) resin as the carbon precursor. Physicochemical studies were carried out for investigating the conversion of RF resin into carbon, and the final cellular monoliths were investigated in terms of elemental composition, total porosity, surface area, micropore volumes, and micro/macropore size distributions. Electrical and electromagnetic (EM) properties were investigated in the static regime and in the Ka-band, respectively. Due to the phenolic nature of the resin, the resultant carbon was glasslike, and the special preparation protocol that was used led to cellular materials whose cell size increased with density. The materials were shown to be relevant for EM shielding, and the relationships between those properties and the density/cell size of those cellular monoliths were elucidated.

Towards a feasible and scalable production of bio-xerogels.

HYPOTHESIS: The synthesis process of carbon xerogels is limited, mainly due to two drawbacks that prevent their introduction onto the market: (i) the long time required for producing the material and (ii) the reagents used for the synthesis, which are costly and harmful to the environment. Microwave radiation is expected to produce a reduction in time of more than 90%, while the use of tannin instead of resorcinol will probably result in a cost-effective carbonaceous material. EXPERIMENTS: Resorcinol-tannin-formaldehyde xerogels containing different amounts of tannin, either with or without a surfactant (sodium dodecyl sulphate), were synthesized by means of two different heating methods: conventional and microwave heating. The effects of the surfactant, the heating method and the addition of tannin upon the porous structure and the chemical composition of the final materials were evaluated. FINDINGS: It was found that the addition of surfactant is essential for obtaining highly porous xerogels when using tannins. The heating method also plays an important role, as conventionally synthesized samples display a greater volume of large pores. However, tannins are less sensitive to microwave radiation and their use results in tannin-formaldehyde xerogels that have a porous structure and chemical composition similar to those of resorcinol-formaldehyde xerogels.

Systematic studies of tannin-formaldehyde aerogels: preparation and properties.

Gelation of tannin-formaldehyde (TF) solutions was systematically investigated by changing pH and concentration of TF resin in water. In this way we constructed the TF phase diagram, from which chemical hydrogels could be described, and also synthesized thermoreversible tannin-based hydrogels. Conditions of non-gelation were also determined. Hydrogels were dried in supercritical CO2, leading to a broad range of TF aerogels. The latter were investigated for volume shrinkage, total porosity, micro-, meso- and macropore volumes, Brunauer-Emmett-Teller (BET) surface area, microscopic texture, mechanical and thermal properties. All these properties are discussed in relation to each other, leading to an accurate and self-consistent description of these bioresource-based highly porous materials. The conditions for obtaining the highest BET surface area or mesopore volume were determined and explained in relation to the preparation conditions. The highest BET surface area, 880 m2 g-1, is remarkably high for organic aerogels derived from a natural resource.

Diffusive and subdiffusive dynamics of indoor microclimate: a time series modeling.

The indoor microclimate is an issue in modern society, where people spend about 90% of their time indoors. Temperature and relative humidity are commonly used for its evaluation. In this context, the two parameters are usually considered as behaving in the same manner, just inversely correlated. This opinion comes from observation of the deterministic components of temperature and humidity time series. We focus on the dynamics and the dependency structure of the time series of these parameters, without deterministic components. Here we apply the mean square displacement, the autoregressive integrated moving average (ARIMA), and the methodology for studying anomalous diffusion. The analyzed data originated from five monitoring locations inside a modern office building, covering a period of nearly one week. It was found that the temperature data exhibited a transition between diffusive and subdiffusive behavior, when the building occupancy pattern changed from the weekday to the weekend pattern. At the same time the relative humidity consistently showed diffusive character. Also the structures of the dependencies of the temperature and humidity data sets were different, as shown by the different structures of the ARIMA models which were found appropriate. In the space domain, the dynamics and dependency structure of the particular parameter were preserved. This work proposes an approach to describe the very complex conditions of indoor air and it contributes to the improvement of the representative character of microclimate monitoring.

Bimodal activated carbons derived from resorcinol-formaldehyde cryogels.

Resorcinol-formaldehyde cryogels prepared at different dilution ratios have been activated with phosphoric acid at 450 °C and compared with their carbonaceous counterparts obtained by pyrolysis at 900 °C. Whereas the latter were, as expected, highly mesoporous carbons, the former cryogels had very different pore textures. Highly diluted cryogels allowed preparation of microporous materials with high surface areas, but activation of initially dense cryogels led to almost non-porous carbons, with much lower surface areas than those obtained by pyrolysis. The optimal acid concentration for activation, corresponding to stoichiometry between molecules of acid and hydroxyl groups, was 2 M l-1, and the acid-cryogel contact time also had an optimal value. Such optimization allowed us to achieve surface areas and micropore volumes among the highest ever obtained by activation with H3PO4, close to 2200 m2 g-1 and 0.7 cm3 g-1, respectively. Activation of diluted cryogels with a lower acid concentration of 1.2 M l-1 led to authentic bimodal activated carbons, having a surface area as high as 1780 m2 g-1 and 0.6 cm3 g-1 of microporous volume easily accessible through a widely developed macroporosity.

Application of a sensor system for determining the kind and quantity of two component VOC mixtures in air after the use of solvents.

This work aims at estimating the ability of a sensor system to recognize the type and to determine the concentration of organic compounds emitted from solvents. The measuring system used in the experiment consisted of a sensor array and a unit for data analysis. Commercially available sensors (Figaro, TGS) were used as a basis for the construction of the first of the two elements. The second element utilized Discriminant Function Analysis (DFA) and Multiple Linear Regression (MLR). The investigated gas samples consisted of air mixed with variable amounts of two organic compounds. One of the two compounds was intended to have a dominant influence on the sensors' response. Depending on this substance, two groups of gaseous mixtures were considered. In the first one, hexane was the major compound. The choice of this particular group of chemical compositions was inspired by practical problems related to various applications of extraction solvents. In the case of the second group of gas mixtures, the prevailing compound was toluene. This type of mixture, on the other hand, is related to the use of paints. The data analysis unit was designed to achieve the following two goals: in the first place, to recognize binary gas mixtures and secondly, to determine the concentrations of VOCs in them. The data analysis was based on steady-state sensor responses. Our study demonstrates that the sensor system may be employed for classifying and quantitatively determining volatile organic compounds released into air as a result of the use of solvents. The presented solution proves the concept in the case of binary mixtures of VOCs.

Recognition of benzene, toluene and xylene using TGS array integrated with linear and non-linear classifier.

Three volatile organic compounds (VOCs): benzene, toluene and xylene were measured with an array of six Taguchi gas sensors in the air with variable humidity content. The recognition of single compounds was performed, based on measurement results. The principal component analysis (PCA) pointed at humidity as the main classification factor in the measurement data set. The linear discriminant analysis (LDA) was applied to overcome this drawback and enforce classification with respect to benzene, toluene or xylene. It was shown that discriminant function analysis (DFA), which is an LDA method allowed for 100% success rate in test samples recognition of benzene. It did not allow for accurate recognition of test samples of toluene or xylene. Following, the non-linear classifier, radial basis function neural network (RBFNN) was applied. A specific configuration of input 's was found, which provided for successful recognition of each single compound: benzene, toluene or xylene in air with variable humidity content.