This work is a comprehensive review of sensing materials, which interact with several target gases pertinent to agricultural monitoring applications. Sensing materials which interact with carbon dioxide, water vapor (relative humidity), hydrogen sulfide, ethylene and ethanol are the focus of this work. Performance characteristics such as dynamic range, recovery time, operating temperature, long-term stability and method of deposition are discussed to determine the commercial viability of the sensing materials considered in this work.
In addition to the sensing materials, deposition methods are considered to obtain the desired sensing material thickness based on the sensor’s mechanism of operation.
Various material classes including metal oxides, conductive polymers and carbon allotropes are included in this review. By implementing multiple sensing materials to detect a single target analyte, the issue of selectivity due to cross sensitivity can be mitigated.
For this reason, where possible, it is desirable to utilize more than one sensing material to monitor a single target gas. Among those considered in this work, it is observed that PEDOT PSS/graphene and TiO2-coated g-C3N4 NS are best suited for CO2 detection, given their wide dynamic range and modest operating temperature. To monitor the presence of ethylene, BMIM-NTf2, SWCNTs and PtTiO2 offer a dynamic range most suitable for the application and require no active heating.
Due to the wide dynamic range offered by SiO2/Si nanowires, this material is best suited for the detection of ethanol; a gas artificially introduced to Gentaur CO2, Temperature & Humidity Monitoring prolong the shelf life of the harvested crop. Finally, among all other sensing materials investigated, it was observed that both SWCNTs and CNTs/SnO2/CuO are most suitable for H2S detection in the given application.
Occurrence of Fusarium spp. in Maize Grain Harvested in Portugal and Accumulation of Related Mycotoxins during Storage
Maize is an important worldwide commodity susceptible to fungal contamination in the field, at harvest, and during storage. This work aimed to determine the occurrence of Fusarium spp. in maize grains produced in the Tagus Valley region of Portugal and the levels of related mycotoxins in the 2018 harvest and during their storage for six months in barrels, mimicking silos conditions. Continuous monitoring of temperature, CO2, and relative humidity levels were done, as well as the concentration of mycotoxins were evaluated and correlated with the presence of Fusarium spp. F. verticillioides was identified as the predominant Fusarium species.
Zearalenone, deoxynivalenol and toxin T2 were not found at harvest and after storage. Maize grains showed some variability in the levels of fumonisins (Fum B1 and Fum B2). At the harvest, fumonisin B1 ranged from 1297 to 2037 µg/kg, and fumonisin B2 ranged from 411 to 618 µg/kg. Fumonisins showed a tendency to increase (20 to 40%) during six months of storage. Although a correlation between the levels of fumonisins and the monitoring parameters was not established, CO2 levels may be used to predict fungal activity during storage. The composition of the fungal population during storage may predict the incidence of mycotoxins.
Developing an Automated Gas Sampling Chamber for Measuring Variations in CO2 Exchange in a Maize Ecosystem at Night
The measurement of net ecosystem exchange (NEE) of field maize at a plot-sized scale is of great significance for assessing carbon emissions. Chamber methods remain the sole approach for measuring NEE at a plot-sized scale. However, traditional chamber methods are disadvantaged by their high labor intensity, significant resultant changes in microclimate, and significant impact on the physiology of crops.
- Therefore, an automated portable chamber with an air humidity control system to determinate the nighttime variation of NEE in field maize was developed. The chamber system can automatically open and close the chamber, and regularly collect gas in the chamber for laboratory analysis.
- Furthermore, a humidity control system was created to control the air humidity of the chamber. Chamber performance test results show that the maximum difference between the temperature and humidity outside and inside the chamber was 0.457 °C and 5.6%, respectively, during the NEE measuring period.
- Inside the chamber, the leaf temperature fluctuation range and the maximum relative change of the maize leaf respiration rate were 0.3 to 0.3 °C and 23.2015%, respectively. We verified a series of measurements of NEE using the dynamic and static closed chamber methods.
- The results show a good common point between the two measurement methods (N = 10, R2 = 0.986; and mean difference: △CO2 = 0.079 ).
- This automated chamber was found to be useful for reducing the labor requirement and improving the time resolution of NEE monitoring. In the future, the relationship between the humidity control system and chamber volume can be studied to control the microclimate change more accurately.
Indoor Air Quality Levels in Schools: Role of Student Activities and No Activities
This work describes a methodology for the definition of indoor air quality monitoring plans in schools and above all to improve the knowledge and evaluation of the indoor concentration levels of some chemical pollutants. The aim is to guide interventions to improve the health of students and exposed staff connected with the activities carried out there. The proposed methodology is based on the simultaneous study of chemical (indoor/outdoor PM2.5, NO2, CO2) and physical (temperature, humidity) parameters by means of automatic analyzers coupled with gaseous compounds (benzene, toluene, ethylbenzene, xylenes, formaldehyde and NO2) sampled by denuders.
The important novelty is that all the data were collected daily in two different situations, i.e., during school activities and no-school activities, allowing us to evaluate the exposure of each student or person. The different behaviors of all the measured pollutants during the two different situations are reported and commented on. Finally, a statistical approach will show how the investigated compounds are distributed around the two components of combustion processes and photochemical reactions.
Multi-well, In-Incubator Imaging Platform for Biological Imaging
Typical approaches to biological imaging consist of a tissue growing environment (e.g., incubator) where multiple samples are cultured simultaneously, and a shared central microscopy unit, where images are generated one sample at a time. In the majority of cases, the samples are manually relocated from the incubator to the central microscopy unit. This technique has one major advantage: the ability to use a single high-cost microscope. However, there are several disadvantages:
Firstly, moving the biological samples outside of the incubator unit can contaminate them. Secondly, the specific growing conditions (e.g., temperature, humidity, CO2 concentration) cannot be maintained without additional equipment during the imaging period. Thirdly, this approach cannot monitor transient phenomena (e.g., continuous monitoring of development).
We propose a multi-well, in-incubator imaging platform that eliminates these shortcomings and can be used for bright-field and fluorescent microscopy. This system is mostly 3-D printed, with a cost coming in under $100 per imaging unit (e.g., tissue growing well). The system is Wi-Fi enabled, allowing remote control (of imaging frequency focus adjustment, lighting, and fluorescent imaging, etc.) without removing the system from the incubator. The images are stored in the cloud allowing for off-site analysis. The imaging system is designed for the scalability of hardware and accounts for larger volumes of data output, storage, and processing.
MT-CO2 Antibody |
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| 1-CSB-PA284962 | Cusabio |
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MT-CO2 Antibody |
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| 1-CSB-PA015073LA01HU | Cusabio |
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MT-CO2 Antibody |
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| 1-CSB-PA565356 | Cusabio |
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CO2 Polyclonal Antibody |
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| ES11964-100ul | ELK Biotech | 100ul | 279 EUR |
CO2 Polyclonal Antibody |
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| ES11964-50ul | ELK Biotech | 50ul | 207 EUR |
Anti-CO2 antibody |
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| STJ193122 | St John's Laboratory | 200 µl | 197 EUR |
MT-CO2 Antibody |
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| 36717-100ul | SAB | 100ul | 252 EUR |
CO2 Polyclonal Antibody |
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| ABP58215-003ml | Abbkine | 0.03ml | 158 EUR |
CO2 Polyclonal Antibody |
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| ABP58215-01ml | Abbkine | 0.1ml | 289 EUR |
CO2 Polyclonal Antibody |
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| ABP58215-02ml | Abbkine | 0.2ml | 414 EUR |
MT-CO2 siRNA |
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| 20-abx924873 | Abbexa |
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anti- CO2 antibody |
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| FNab10192 | FN Test | 100µg | 505.25 EUR |
MT- CO2 Antibody |
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| ABD7867 | Lifescience Market | 100 ug | 438 EUR |
MT- CO2 Antibody |
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| ABD8127 | Lifescience Market | 100 ug | 438 EUR |
MT-CO2 Antibody |
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| DF7867 | Affbiotech | 200ul | 304 EUR |
pt1000 temperature compensator |
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| ST10N | Consort | ea | 94 EUR |
pt1000 temperature compensator |
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| ST20N | Consort | ea | 98 EUR |
External Temperature Probe |
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| BSH-TP1 | Benchmark Scientific | 1 PC | 429.53 EUR |
Anti-MT-CO2 antibody |
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| STJ119944 | St John's Laboratory | 100 µl | 277 EUR |
Anti-MT-CO2 antibody |
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| STJ24628 | St John's Laboratory | 100 µl | 393 EUR |
Anti-MT-CO2 antibody |
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| STJ113171 | St John's Laboratory | 100 µl | 277 EUR |
Anti-MT-CO2 antibody |
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| STJ113717 | St John's Laboratory | 100 µl | 277 EUR |
CO2 Gas insert unit |
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| 3600110 | Atto | 1unit | 11193 EUR |
MT-CO2 Rabbit pAb |
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| A17965-100ul | Abclonal | 100 ul | 308 EUR |
MT-CO2 Rabbit pAb |
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| A17965-200ul | Abclonal | 200 ul | 459 EUR |
MT-CO2 Rabbit pAb |
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| A17965-20ul | Abclonal | 20 ul | 183 EUR |
MT-CO2 Rabbit pAb |
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| A17965-50ul | Abclonal | 50 ul | 223 EUR |
MT-CO2 Conjugated Antibody |
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| C36717 | SAB | 100ul | 397 EUR |
MT-CO2 Blocking Peptide |
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| DF7867-BP | Affbiotech | 1mg | 195 EUR |
