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This work reports on the fabrication and analysis of near-infrared and mid-infrared luminescence spectra and their decays in fluoroindate glasses co-doped with Yb3+/Ho3+. The attention has been paid to the analysis of the Yb3+→ Ho3+ energy transfer processed ions in fluoroindate glasses pumped by 976 nm laser diode. The most effective sensitization for 2 μm luminescence has been obtained in glass co-doped with 0.8YbF3/1.6HoF3. Further study in the mid-infrared spectral range (2.85 μm) showed that the maximum emission intensity has been obtained in fluoroindate glass co-doped with 0.1YbF3/1.4HoF3. The obtained efficiency of Yb3+→ Ho3+ energy transfer was calculated to be up to 61% (0.8YbF3/1.6HoF3), which confirms the possibility of obtaining an efficient glass or glass fiber infrared source for a MID-infrared (MID-IR) sensing application.

A wealth of theoretical studies demonstrates p‐type MoS2 (p‐MoS2) as a promising candidate for carbon dioxide (CO2) detection at room temperature. Its applications are retarded by issues associated with its practical chemical synthesis and sensing selectivity. Herein, a chemically tunable strategy is established for in situ growth of p‐MoS2 with controlled thickness and n‐/p‐type transition on N‐ and Fe‐enriched carbon (FeNC) nanosheets. The introduced sulfur vacancies (Svacs) enhance the sensitivity to CO2, and the modulated electron distribution suppresses surface oxygen ionization to improve sensing selectivity. The optimized p‐type composites can detect CO2 fluctuation levels as low as 50 ppm at room temperature. Density functional theory (DFT) and grand canonical Monte Carlo (GCMC) simulations clarify the underlying mechanisms. A visualized machine learning (ML) model is developed using a hybrid ML strategy that generates regression surfaces from linear/nonlinear data. Through this model, a single sensor accurately discriminates CO2 from interfering and predicts its concentration and humidity with accuracies exceeding 95%. An intelligent sensing system capable of environmental monitoring and tracking exhaled CO2 is demonstrated. The measured fluctuations strongly correlate with physiological indicators, underscoring their potential for non‐invasive health monitoring and medical diagnostics. In situ growth of p‐type MoS2 (p‐MoS2) on N‐ and Fe‐enriched carbon (FeNC) nanosheets allows for detection of carbon dioxide (CO2) fluctuation levels as low as 50 ppm at 25 °C. The induced S vacancies (Svacs) and the modulated electron distribution jointly improved sensing selectivity to CO2.

Silver nanoparticles capped with 3-mercapto-1propanesulfonic acid sodium salt (AgNPs-3MPS), able to interact with Ni2+ or Co2+, have been prepared to detect these heavy metal ions in water. This system works as an optical sensor and it is based on the change of the intensity and shape of optical absorption peak due to the surface plasmon resonance (SPR) when the AgNPs-3MPS are in presence of metals ions in a water solution. We obtain a specific sensitivity to Ni2+ and Co2+ up to 500 ppb (part per billion). For a concentration of 1 ppm (part per million), the change in the optical absorption is strong enough to produce a colorimetric effect on the solution, easily visible with the naked eye. In addition to the UV-VIS characterizations, morphological and dimensional studies were carried out by transmission electron microscopy (TEM). Moreover, the systems were investigated by means of dynamic light scattering (DLS), Fourier-transform infrared spectroscopy (FTIR), X-ray photoelectron spectroscopy (XPS), and high-resolution X-ray photoelectron spectroscopy (HR-XPS). On the basis of the results, the mechanism responsible for the AgNPs-3MPS interaction with Ni2+ and Co2+ (in the range of 0.5–2.0 ppm) looks like based on the coordination compounds formation.

Fungi are exposed to various environmental variables during their life cycle, including changes in CO2 concentration. CO2 has the potential to act as an activator of several cell signaling pathways. In fungi, the sensing of CO2 triggers cell differentiation and the biosynthesis of proteins involved in the metabolism and pathogenicity of these microorganisms. The molecular machineries involved in CO2 sensing constitute a promising target for the development of antifungals. Carbonic anhydrases (CAs, EC 4.2.1.1) are crucial enzymes in the CO2 sensing systems of fungi, because they catalyze the reversible hydration of CO2 to proton and HCO3-. Bicarbonate in turn boots a cascade of reactions triggering fungal pathogenicity and metabolism. Accordingly, CAs affect microorganism proliferation and may represent a potential therapeutic target against fungal infection. Here, the inhibition of the unique β-CA (MpaCA) encoded in the genome of Malassezia pachydermatis, a fungus with substantial relevance in veterinary and medical sciences, was investigated using a series of conventional CA inhibitors (CAIs), namely aromatic and heterocyclic sulfonamides. This study aimed to describe novel candidates that can kill this harmful fungus by inhibiting their CA, and thus lead to effective anti-dandruff and anti-seborrheic dermatitis agents. In this context, current antifungal compounds, such as the azoles and their derivatives, have been demonstrated to induce the selection of resistant fungal strains and lose therapeutic efficacy, which might be restored by the concomitant use of alternative compounds, such as the fungal CA inhibitors.

Fiber-optic CO2 sensors are free of electromagnetic interference and highly resistant to corrosion but often require expensive measurement systems or sophisticated preparation procedures. To address this challenge, we developed a CO2 sensor comprising a hetero-core fiber-optic surface plasmon resonance sensor coated with an ionic liquid (IL) gel as a cost-effective CO2 sensing system. Among the various sensors tested, the sensor using ethyl-3-methylimidazolium tetrafluoroborate ([EMIM][BF4]) as the IL, prepared at a pull-up speed of 4.5 mm s−1, showed the best CO2 sensing performance. A hybrid membrane of [EMIM][BF4] and a CO2-absorbing polymer (monoethanolamine) demonstrated higher sensitivity for CO2 concentrations ranging from 0 to 100%. The highlight of the proposed system is its low-cost configuration comprising an 850 nm-wavelength LED and a photodiode as the light source and signal detection device, respectively. Along with facile preparation and low-cost system configuration, the proposed system exhibited significant potential for CO2 sensing in various fields.

In this review we concentrate on guard cell metabolism and CO2 sensing. Although a matter of some controversy, it is generally accepted that the Calvin cycle plays a minor role in stomatal movements. Recent data emphasise the importance of guard cell starch degradation and of carbon import from the guard cell apoplast in promoting and maintaining stomatal opening. Chloroplast maltose and glucose transporters appear to be crucial to the export of carbon from both guard and mesophyll cells. The way guard cells sense CO2 remains an unresolved question. However, a better understanding of the cellular events downstream from CO2 sensing is emerging. We now recognise that there are common as well as unique steps in abscisic acid (ABA) and CO2 signalling pathways. For example, while ABA and CO2 both trigger increases in cytoplasmic free calcium, unlike ABA, CO2 does not promote a cytoplasmic pH change. Future advances in this area are likely to result from the increased use of techniques and resources, such as, reverse genetics, novel mutants, confocal imaging, and microarray analyses of the guard cell transcriptome.

We present a tunable diode-laser absorption spectroscopy (TDLAS) system operating at 1.5711 µm for CO2 gas concentration measurements. The system can operate in either a traditional direct-mode (dTDLAS) sawtooth wavelength scan or a recently demonstrated wavelength-toggled single laser differential-absorption lidar (WTSL-DIAL) mode using precompensated current pulses. The use of such precompensated pulses offsets the slow thermal constants of the diode laser, leading to fast toggling between ON and OFF-resonance wavelengths. A short measurement time is indeed pivotal for atmospheric sensing, where ambient factors, such as turbulence or mechanical vibrations, would otherwise deteriorate sensitivity, precision and accuracy. Having a system able to operate in both modes allows us to benchmark the novel experimental procedure against the well-established dTDLAS method. The theory behind the new WTSL-DIAL method is also expanded to include the periodicity of the current modulation, fundamental for the calculation of the OFF-resonance wavelength. A two-detector scheme is chosen to suppress the influence of laser intensity fluctuations in time (1/f noise), and its performance is eventually benchmarked against a one-detector approach. The main difference between dTDLAS and WTSL-DIAL, in terms of signal processing, lies in the fact that while the former requires time-consuming data processing, which limits the maximum update rate of the instrument, the latter allows for computationally simpler and faster concentration readings. To compare other performance metrics, the update rate was kept at 2 kHz for both methods. To analyze the dTDLAS data, a four-parameter Lorentzian fit was performed, where the fitting function comprised the six main neighboring absorption lines centered around 1.5711 µm. Similarly, the spectral overlap between the same lines was considered when analyzing the WTSL-DIAL data in real time. Our investigation shows that, for the studied time intervals, the WTSL-DIAL approach is 3.65 ± 0.04 times more precise; however, the dTDLAS-derived CO2 concentration measurements are less subject to systematic errors, in particular pressure-induced ones. The experimental results are accompanied by a thorough explanation and discussion of the models used, as well as their advantages and limitations.

Indoor air quality (IAQ) in houses is often deteriorated by chemical substances emitted from heating, building materials, or other household goods. Since it is difficult for occupants to recognize air pollution, they rarely understand the actual conditions of the IAQ. An investigation into the actual condition of IAQ in houses was therefore conducted in this study. Carbon dioxide (CO2) concentrations in 24 occupied houses was measured, and the results from our analysis showed that the use of combustion heaters increased the concentration of CO2 and led to indoor air pollution. Results indicate that as outdoor temperature decreased, the frequency of ventilation decreased simultaneously, and CO2 concentration increased. Results of the questionnaire survey revealed that the actual IAQ in each house did not match the level of awareness its occupants had regarding ventilation. Along with this difficulty in perceiving air pollution, the lack of knowledge about ventilation systems and the effects of combustion heating may be additional barriers to IAQ awareness.

We developed a Raman lidar system that can remotely detect CO2 leakage and its volume mixing ratio (VMR). The system consists of a laser, a telescope, an optical receiver, and detectors. Indoor CO2 cell measurements show that the accuracy of the Raman lidar is 99.89%. Field measurements were carried out over a four-day period in November 2017 at the Eumsong Environmental Impact Evaluation Test Facility (EIT), Korea, where a CO2 leak was located 0.2 km from the Raman lidar. The results show good agreement between CO2 VMR measured by the Raman lidar system (CO2 VMRRaman LIDAR) and that measured by in situ instruments (CO2 VMRIn-situ). The correlation coefficient (R), mean absolute error (MAE), root mean square error (RMSE), and percentage difference between CO2 VMRIn-situ and CO2 VMRRaman LIDAR are 0.81, 0.27%, 0.37%, and 4.92%, respectively. The results indicate that Raman lidar is an effective tool in detecting CO2 leakage and in measuring CO2 VMR remotely.

Since September 2014, NASA's Orbiting Carbon Observatory-2 (OCO-2) satellite has been taking measurements of reflected solar spectra and using them to infer atmospheric carbon dioxide levels. This work provides details of the OCO-2 retrieval algorithm, versions 7 and 8, used to derive the column-averaged dry air mole fraction of atmospheric CO2 (XCO2) for the roughly 100 000 cloud-free measurements recorded by OCO-2 each day. The algorithm is based on the Atmospheric Carbon Observations from Space (ACOS) algorithm which has been applied to observations from the Greenhouse Gases Observing SATellite (GOSAT) since 2009, with modifications necessary for OCO-2. Because high accuracy, better than 0.25 %, is required in order to accurately infer carbon sources and sinks from XCO2, significant errors and regional-scale biases in the measurements must be minimized. We discuss efforts to filter out poor-quality measurements, and correct the remaining good-quality measurements to minimize regional-scale biases. Updates to the radiance calibration and retrieval forward model in version 8 have improved many aspects of the retrieved data products. The version 8 data appear to have reduced regional-scale biases overall, and demonstrate a clear improvement over the version 7 data. In particular, error variance with respect to TCCON was reduced by 20 % over land and 40 % over ocean between versions 7 and 8, and nadir and glint observations over land are now more consistent. While this paper documents the significant improvements in the ACOS algorithm, it will continue to evolve and improve as the CO2 data record continues to expand.