Model-based development of cell voltage control system for modified bio-electro-Fenton process.

A modified bio-electro-Fenton (M-BEF) process with a cell voltage control system that improves the efficiency of organic removal and energy savings is demonstrated. The M-BEF process can accomplish bioelectricity generation, H2O2 production, and the Fenton reaction in a continuous-flow reactor. During synthetic wastewater treatment containing biodegradable (glucose) and recalcitrant (biphenyl) organic matter, the effluent chemical oxygen demand (COD) concentration was maintained between 2 and 6 mg L-1. To investigate the impact of different operating schemes on energy usage, model-based design (MBD) modeling and simulations were performed, which showed that COD removal efficiency without an external voltage supply was unstable at < 70 %. The automatic cell voltage control system saved 90 % of the power compared to the continuous cell voltage supply system. Further testing on more environmental samples and pollutants will enable real-time optimization of supplied power and wastewater treatment using the cell voltage control system.

Multiple-layer statistical methodology for developing data-driven models of anaerobic digestion process.

When modelling anaerobic digestion, ineffective data handling and inadequate designation of modelling parameters can undermine the model reliability. In this study, a multilayer statistical technique, which employed a machine learning technique using regression models, was introduced to systematically support the development of anaerobic digestion models. Layer-by-layer statistical techniques including cubic smoothing splines (missing data reconstruction), principal component analysis (identifying correlated parameters), analysis of variance (analysing differences among datasets), and linear regression (developing data-driven models) were used to develop and validate anaerobic digestion models. Experimental data collected from the long-term operation of lab-scale (operated for 350 days), pilot-scale (operated for 150 days), and full-scale reactors (operated for 750 days) were used to demonstrate the modelling process. The multivariate models based on a data-driven modelling technique were developed by subjecting the experimental and monitored data to a modelling process. The developed models could predict the biogas production and effluent chemical oxygen demand during anaerobic digestion. Statistical analyses verified the modelling hypotheses, evaded invalid model development, and ensured data integrity and parameter validity. Multiple linear regression of principal components demonstrated that the performance of biogas production using food waste was influenced by the variances of the nitrogen and organic concentrations, but not by the chemical oxygen demand to total nitrogen (C/N) ratio. In the validation process, the model developed with lab-scale reactor data showed relatively high accuracy with R2, SSE, and RMSE values of 0.86, 34.45, and 0.72.

Single-Cell Detection of Erwinia amylovora Using Bio-Functionalized SIS Sensor.

Herein, we developed a bio-functionalized solution-immersed silicon (SIS) sensor at the single-cell level to identify Erwinia amylovora (E. amylovora), a highly infectious bacterial pathogen responsible for fire blight, which is notorious for its rapid spread and destructive impact on apple and pear orchards. This method allows for ultra-sensitive measurements without pre-amplification or labeling compared to conventional methods. To detect a single cell of E. amylovora, we used Lipopolysaccharide Transporter E (LptE), which is involved in the assembly of lipopolysaccharide (LPS) at the surface of the outer membrane of E. amylovora, as a capture agent. We confirmed that LptE interacts with E. amylovora via LPS through in-house ELISA analysis, then used it to construct the sensor chip by immobilizing the capture molecule on the sensor surface modified with 3'-Aminopropyl triethoxysilane (APTES) and glutaraldehyde (GA). The LptE-based SIS sensor exhibited the sensitive and specific detection of the target bacterial cell in real time. The dose-response curve shows a linearity (R2 > 0.992) with wide dynamic ranges from 1 to 107 cells/mL for the target bacterial pathogen. The sensor showed the value change (dΨ) of approximately 0.008° for growing overlayer thickness induced from a single-cell E. amylovora, while no change in the control bacterial cell (Bacillus subtilis) was observed, or negligible change, if any. Furthermore, the bacterial sensor demonstrated a potential for the continuous detection of E. amylovora through simple surface regeneration, enabling its reusability. Taken together, our system has the potential to be applied in fields where early symptoms are not observed and where single-cell or ultra-sensitive detection is required, such as plant bacterial pathogen detection, foodborne pathogen monitoring and analysis, and pathogenic microbial diagnosis.

Highly Sensitive and Specific Detection of Influenza A Viruses Using Bimolecular Fluorescence Complementation (BiFC) Reporter System.

In this study, we developed a highly sensitive and specific bimolecular fluorescence complementation (BiFC)-based influenza A virus (IAV)-sensing system by combining a galactose/glucose-binding protein (GGBP) with an N-terminal large domain (YN1-172) and a C-terminal small domain (YC173-239) made up of enhanced yellow fluorescence protein (eYFP). The GGBP-based BiFC reporter exhibits the fluorescence reconstitution as a result of conformational changes in GGBP when lactose, which was derived from 6'-silalyllactose and used as a substrate for neuraminidase (NA), binds to GGBP in the presence of IAV. The system showed a linear dynamic range extending from 1 × 100 to 1 × 107 TCID50/mL, and it had a detection limit of 1.1 × 100 TCID50/mL for IAV (H1N1), demonstrating ultra-high sensitivity. Our system exhibited fluorescence intensity enhancements in the presence of IAV, while it displayed weak fluorescence signals when exposed to NA-deficient viruses, such as RSV A, RSV B, adenovirus and rhinovirus, thereby indicating selective responses for IAV detection. Overall, our system provides a simple, highly sensitive and specific IAV detection platform based on BiFC that is capable of detecting ligand-induced protein conformational changes, obviating the need for virus culture or RNA extraction processes.

Occurrence and sources of micro-plastics in various water bodies, sediments, and fishes in Ansan, South Korea.

In this study, the Pearson correlation coefficients were determined to derive correlations between micro-plastics (MPs) in carp and river crabs. MPs were detected for various water sources, including four rivers and four main waterways, sediments, and fish, using Fourier transform infrared spectrometry (FTIR), microscopic analysis, and image mapping. Carp and river crabs had coefficients of 0.888 and 0.724, respectively, which showed a high positive correlation. In water samples, the MPs detected in rivers were higher than those in the main waterway. However, in sediment samples, the MPs detected in the main waterway were higher than those in the rivers. It is believed that MPs are carried toward shore by ocean tide. The size of most of the sediment MPs was 20-49 µm, representing 64.1% of the entire population. The plastics detected in this study were polyethylene terephthalate (PET), polypropylene (PP), and polyethylene (PE), which originate from synthetic fibers, scrubs, and packing material. MP pollution by non-point pollution sources was investigated, with the abundance of MPs increasing by 2 to 3 times between the dry and wet seasons in water and sediment, respectively. It was determined that the inflow of MPs into rivers could have been due to non-point source pollutants from household items, roads, plants, and soil around the water sources.

Identification of bacterial communities in conventional wastewater treatment sludge to inform inoculation of the anammox process.

This study uses 16 S rRNA gene pyrosequencing for the identification of a vast number of wastewater bacterial communities to investigate the evolution of bacterial communities in the Anammox process. Four lab-scale Anammox reactors inoculated with different conventional wastewater treatment sludge (activated sludge, livestock wastewater treatment sludge, denitrification sludge, and anaerobic digestion sludge) were operated under identical operating conditions for more than 400 days. The phylum Planctomycetes was present in all seeds of conventional sludge with a relative abundance of 1-3%. In particular, the known Anammox bacteria Candidatus Brocadia was found in the seed of the denitrification sludge. The reactor inoculated with denitrification sludge demonstrated the most effective nitrogen removal of ∼80% with successful cultivation of Anammox bacteria. This study found that the performance of the Anammox process is related to the presence of Nitrospira genus (nitrite-oxidizing bacteria) and that symbiotic association with other functional groups can lead to nitrogen removal. The outcomes of this study can provide vital insight into the study of microbial ecology for the cultivation of Anammox bacteria.

Highly Selective FRET-Aided Single-Molecule Counting of MicroRNAs Labeled by Splinted Ligation.

MicroRNAs (miRNAs) are short non-coding RNAs that play an important role in regulating gene expression. Since miRNAs are abnormally expressed in various cancers, they are considered to be promising biomarkers for early cancer diagnosis. However, the short length and strong sequence similarity among miRNAs make their reliable quantification very challenging. We developed a highly selective amplification-free miRNA detection method based on Förster resonance energy transfer (FRET)-aided single-molecule counting. miRNAs were selectively labeled with FRET probes using splinted ligation. When imaged with a single-molecule FRET setup, the miRNA molecules were accurately identified by the probe's FRET. miRNA concentrations were estimated from the count of molecules. The high sensitivity of the method in finding sparse molecules enabled us to achieve a limit of detection of 31-56 amol for miR-125b, miR-100, and miR-99a. Single nucleotide mismatch could be discriminated with a very high target-to-mismatch ratio. The method accurately measured the high expression of miR-125b in gastric cancer cells, which agreed well with previous reports. The high sensitivity and accuracy of this technique demonstrated its clinical potential as a robust miRNA detection method.

Wireless portable bioelectronic nose device for multiplex monitoring toward food freshness/spoilage.

Monitoring food freshness/spoilage is important to ensure food quality and safety. Current methods of food quality monitoring are mostly time-consuming and labor intensive processes that require massive analytical equipment. In this study, we developed a portable bioelectronic nose (BE-nose) integrated with trace amine-associated receptor (TAAR) nanodiscs (NDs), allowing food quality monitoring via the detection of food spoilage indicators, including the biogenic amines cadaverine (CV) and putrescine (PT). The olfactory receptors TAAR13c and TAAR13d, which have specific affinities for CV and PT, were produced and successfully reconstituted in ND structures. TAAR13 NDs BE-nose-based side-gated field-effect transistor (SG-FET) system was constructed by utilizing a graphene micropattern (GM) into which two types of olfactory NDs (TAAR13c ND and TAAR13d ND) were introduced, and this system showed ultrahigh sensitivity for a limit of detection (LOD) of 1 fM for CV and PT. Moreover, the binding affinities between the TAAR13 NDs and the indicators were confirmed by a tryptophan fluorescence quenching assay and biosimulations, in which the specific binding site was confirmed. Gas-phase indicators were detected by the TAAR13 NDs BE-nose platform, and the LODs for CV and PT were confirmed to be 26.48 and 7.29 ppb, respectively. In addition, TAAR13 NDs BE-nose was fabricated with commercial gas sensors as a portable platform for the measurement of NH3 and H2S, multiplexed monitoring was achieved with similar performance, and the change ratio of the indicators was observed in a real sample. The integration of commercial gas sensors on a BE-nose enhanced the accuracy and reliability for the quality monitoring of real food samples. These results indicate that the portable TAAR13 NDs BE-nose can be used to monitor CV and PT over a wide range of concentrations, therefore, the electronic nose platform can be utilized for monitoring the freshness/spoilage step in various foods.

Global forest management data for 2015 at a 100 m resolution.

Spatially explicit information on forest management at a global scale is critical for understanding the status of forests, for planning sustainable forest management and restoration, and conservation activities. Here, we produce the first reference data set and a prototype of a globally consistent forest management map with high spatial detail on the most prevalent forest management classes such as intact forests, managed forests with natural regeneration, planted forests, plantation forest (rotation up to 15 years), oil palm plantations, and agroforestry. We developed the reference dataset of 226 K unique locations through a series of expert and crowdsourcing campaigns using Geo-Wiki ( https://www.geo-wiki.org/ ). We then combined the reference samples with time series from PROBA-V satellite imagery to create a global wall-to-wall map of forest management at a 100 m resolution for the year 2015, with forest management class accuracies ranging from 58% to 80%. The reference data set and the map present the status of forest ecosystems and can be used for investigating the value of forests for species, ecosystems and their services.

Application of integrated Korean forest growth dynamics model to meet NDC target by considering forest management scenarios and budget.

BACKGROUND: Forests are atmospheric carbon sinks, whose natural growth can contribute to climate change mitigation. However, they are also affected by climate change and various other phenomena, for example, the low growth of coniferous forests currently reported globally, including in the Republic of Korea. In response to the implementation of the Paris Agreement, the Korean government has proposed 2030 greenhouse gas roadmap to achieve a Nationally Determined Contribution (NDC), and the forest sector set a sequestration target of 26 million tons by 2030. In this study, the Korean forest growth model (KO-G-Dynamic model) was used to analyze various climate change and forest management scenarios and their capacity to address the NDC targets. A 2050 climate change adaptation strategy is suggested based on forest growth and CO2 sequestration. RESULTS: Forest growth was predicted to gradually decline, and CO2 sequestration was predicted to reach 23 million tons per year in 2050 if current climate and conditions are maintained. According to the model, sequestrations of 33 million tCO2 year-1 in 2030 and 27 million tCO2 year-1 in 2050 can be achieved if ideal forest management is implemented. It was also estimated that the current forest management budget of 317 billion KRW (264 million USD) should be twice as large at 722 billion KRW (602 million USD) in the 2030s and 618 billion KRW (516 million USD) in the 2050s to achieve NDC targets. CONCLUSIONS: The growth trend in Korea's forests transitions from young-matured stands to over-mature forests. The presented model-based forest management plans are an appropriate response and can increase the capacity of Korea to achieve its NDC targets. Such a modeling can help the forestry sector develop plans and policies for climate change adaptation.

Application of data smoothing and principal component analysis to develop a parameter ranking system for the anaerobic digestion process.

A parameter ranking system to enhance data interpretation of the anaerobic digestion process was developed using principal component analysis (PCA) and data smoothing. The experimental data collected from the start-up operation of a pilot-scale, two-stage anaerobic digester for food wastewater treatment was used to demonstrate the enhanced PCA procedures. The correlation of multiple parameters in the anaerobic digestion process could be identified by the ranked parameters based on statistically scored outcomes. According to the parameters ranked for their impact on biogas production and methane yield, the biochemical oxygen demand (BOD) in the food wastewater was shown to have the highest correlated ranks with a score of 0.246, and the pH and ammonium in the food wastewater were shown to have lower ranked scores of 0.834 and 1.019, respectively. Therefore, ammonia toxicity and pH shock might not have had significant influence, and the operating priority should be focused on the organic loads. This application of PCA to anaerobic digestion can be helpful for addressing stabilization issues by identifying the core parameters that primarily contribute to early detection of operating problems.

Genetically Modified Ferritin Nanoparticles with Bone-Targeting Peptides for Bone Imaging.

Bone homeostasis plays a major role in supporting and protecting various organs as well as a body structure by maintaining the balance of activities of the osteoblasts and osteoclasts. Unbalanced differentiation and functions of these cells result in various skeletal diseases, such as osteoporosis, osteopetrosis, and Paget's disease. Although various synthetic nanomaterials have been developed for bone imaging and therapy through the chemical conjugation, they are associated with serious drawbacks, including heterogeneity and random orientation, in turn resulting in low efficiency. Here, we report the synthesis of bone-targeting ferritin nanoparticles for bone imaging. Ferritin, which is a globular protein composed of 24 subunits, was employed as a carrier molecule. Bone-targeting peptides that have been reported to specifically bind to osteoblast and hydroxyapatite were genetically fused to the N-terminus of the heavy subunit of human ferritin in such a way that the peptides faced outwards. Ferritin nanoparticles with fused bone-targeting peptides were also conjugated with fluorescent dyes to assess their binding ability using osteoblast imaging and a hydroxyapatite binding assay; the results showed their specific binding with osteoblasts and hydroxyapatite. Using in vivo analysis, a specific fluorescent signal from the lower limb was observed, demonstrating a highly selective affinity of the modified nanoparticles for the bone tissue. These promising results indicate a specific binding ability of the nanoscale targeting system to the bone tissue, which might potentially be used for bone disease therapy in future clinical applications.

Multi-Odor Discrimination by Rat Sniffing for Potential Monitoring of Lung Cancer and Diabetes.

The discrimination learning of multiple odors, in which multi-odor can be associated with different responses, is important for responding quickly and accurately to changes in the external environment. However, very few studies have been done on multi-odor discrimination by animal sniffing. Herein, we report a novel multi-odor discrimination system by detection rats based on the combination of 2-Choice and Go/No-Go (GNG) tasks into a single paradigm, in which the Go response of GNG was replaced by 2-Choice, for detection of toluene and acetone, which are odor indicators of lung cancer and diabetes, respectively. Three of six trained rats reached performance criterion, in 12 consecutive successful tests within a given set or over 12 sets with a success rate of over 90%. Through a total of 1300 tests, the trained animals (N = 3) showed multi-odor sensing performance with 88% accuracy, 87% sensitivity and 90% specificity. In addition, a dependence of behavior response time on odor concentrations under given concentration conditions was observed, suggesting that the system could be used for quantitative measurements. Furthermore, the animals' multi-odor sensing performance has lasted for 45 days, indicating long-term stability of the learned multi-odor discrimination. These findings demonstrate that multi-odor discrimination can be achieved by rat sniffing, potentially providing insight into the rapid, accurate and cost-effective multi-odor monitoring in the lung cancer and diabetes.

Olfactory Detection of Toluene by Detection Rats for Potential Screening of Lung Cancer.

Early detection is critical to successfully eradicating a variety of cancers, so the development of a new cancer primary screening system is essential. Herein, we report an animal nose sensor system for the potential primary screening of lung cancer. To establish this, we developed an odor discrimination training device based on operant conditioning paradigms for detection of toluene, an odor indicator component of lung cancer. The rats (N = 15) were trained to jump onto a floating ledge in response to toluene-spiked breath samples. Twelve rats among 15 trained rats reached performance criterion in 12 consecutive successful tests within a given set, or over 12 sets, with a success rate of over 90%. Through a total of 1934 tests, the trained rats (N = 3) showed excellent performance for toluene detection with 82% accuracy, 83% sensitivity, 81% specificity, 80% positive predictive value (PPV) and 83% negative predictive value (NPV). The animals also acquired considerable performance for odor discrimination even in rigorous tests, validating odor specificity. Since environmental and long-term stability are important factors that can influence the sensing results, the performance of the trained rats was studied under specified temperature (20, 25, and 30 °C) and humidity (30%, 45%, and 60% RH) conditions, and monitored over a period of 45 days. At given conditions of temperature and humidity, the animal sensors showed an average accuracy within a deviation range of ±10%, indicating the excellent environmental stability of the detection rats. Surprisingly, the trained rats did not differ in retention of last odor discrimination when tested 45 days after training, denoting that the rats' memory for trained odor is still available over a long period of time. When taken together, these results indicate that our odor discrimination training system can be useful for non-invasive breath testing and potential primary screening of lung cancer.

Application of principal component analysis (PCA) to the assessment of parameter correlations in the partial-nitrification process using aerobic granular sludge.

For the first time, principal component analysis (PCA) was used to extract relevant information hidden in the partial-nitrification process using aerobic granular sludge. The objectives of this research are (a) to determine total ammonia nitrogen (TAN), total nitrite nitrogen (NO2-N), nitrate nitrogen (NO3-N), and other water quality parameters; (b) to identify the diversity of nitrification and denitrification bacterial community of wastewater samples during the partial-nitrification process using aerobic granular sludge and; (c) to analyze the correlation of available parameters using PCA. The nitrite accumulation ratio was determined from TAN, NO2-N, and NO3-N. Other water quality parameters were mixed liquor volatile suspended solids (MLVSS), alkalinity, total nitrogen (TN) and sludge volume index (SVI), pH, and dissolved oxygen (DO). The identification of bacterial community was conducted using 16S rRNA gene-based pyrosequencing by GS Junior Sequencing system. The water quality parameters were computed for PCA using software MATLAB. A nitrite accumulation ratio (NAR) between 0.55 and 0.85 was determined while maintaining the aerobic granular sludge's compact and dense structure. The PCA was used to reduce the data dimensionality from the original 8 variables to 2 principal components explaining 75% of the total data variance. Applying PCA to the data analysis in biological wastewater treatment can support detecting data anomalies and separating useful information from unwanted interferences.

Pseudo-analytical solutions for multi-species biofilm model of aerobic granular sludge.

This paper demonstrates modelling of the aerobic granular sludge (AGS) process with the pseudo-analytical solutions (PAS) of a biofilm model. A MATLAB programmed graphical user interface platform was developed to facilitate the model calculation and access. Model calibration and validation were carried out through using experimental data collected from a granular sludge sequencing batch reactor operation. The experimental and modelling results identified the distribution of heterotrophs and nitrifiers on the AGS and its contribution to the performance of wastewater treatment. The model could describe multi-species biofilms according to the distinguishing features among the three levels of PAS models. The models demonstrated increasing degrees of interaction (no interaction, competition for nitrogen and layering and protection) between heterotrophs and nitrifiers. Modelling the AGS process using PAS increases the accessibility of the simulation of multiple species in both biofilm and suspended biomass.

Effect of hydrothermal pre-treatment on physical properties and co-digestion from food waste and sewage sludge mixture.

Anaerobic digestion (AD) is generally considered to be an economic and environmentally friendly technology for treating waste activated sludge, but has some limitations, such as the time it takes for the sludge to be digested and also the ineffectiveness of degrading the solids. Various pre-treatment technologies have been suggested to overcome these limitations and to improve the biogas production rate by enhancing the hydrolysis of organic matter. This paper studies the use of hydrothermal pre-treatment (HTP) for a food waste and sewage sludge mixture (FW-SS mixture) as pre-treatment of co-digestion. The results of the capillary suction time, time to filter, and particle size decreased with increasing HTP temperature. These results of the assessment that was conducted in this study confirm that the HTP process indeed modifies the physical properties of the FW-SS mixture to enhance the solubilization of organic solids. A maximum increase in biogas production of 50% is achieved with a HTP temperature of 140oC. These findings show that to achieve high conversion efficiency, an accurately designed pre-treatment step must be included in the overall AD process for wastewater treatment.

Optimizing pre-treatment conditions for anaerobic co-digestion of food waste and sewage sludge.

In this study, the optimum conditions of thermal-alkali pre-treatment and the performance of ammonia stripping were investigated for improving solubilization efficiency and methane yield in the anaerobic co-digestion of food waste (FW) and sewage sludge (SS). The reaction temperature, NaOH concentration and reaction time for the thermal-alkali pre-treatment were investigated to determine optimum pre-treatment conditions. Solubilization rate, volatile suspended solids (VSS) reduction rate and total volatile fatty acid (VFAs) yields were improved with increasing reaction temperature, NaOH concentration and reaction time. In addition, by applying the optimum pre-treatment conditions (140 °C, 60 meq/L and 60 min), the experimental methane yield of thermal-alkali pre-treatment of a mixture of FW and SS was 483.0 ±â€¯15.7 mL CH4/g VSadded, which was about 84% higher than that of the untreated one. However, after thermal-alkali pre-treatment, the NH4+ concentration of the thermal-alkali pre-treatment liquid showed a concentration that could inhibit anaerobic digestion, so ammonia stripping was performed to remove NH4+. As a result, the experimental methane yield was increased by about 7% compared to when ammonia stripping was not performed.

Application of curve-fitting techniques to develop numerical calibration procedures for a river water quality model.

River water quality models are often constrained by a lack of understanding of model structures and complicated estimation procedures for unknown parameters. This paper demonstrates a new calibration strategy by setting up a simple model structure for river water quality. The unknown parameters of RWQM were calibrated through the use of small river water quality data sets. In order to facilitate the calibration procedure, data reconstruction and parameter estimation were performed by the systematic application of cubic smoothing spline, polynomial curve-fitting and nonlinear least squares. The quality of calibrated parameters was estimated by developing a sensitivity ranking system. The variation of model outputs showed a slight difference at a sensitivity index of less than 10% and a significant difference at a sensitivity index of more than 50%. The one-way analysis of variance showed a large p-value of 0.8431, indicating that differences between model data and measured data means are not significant. The calibrated model responses and their statistical envelopes were in good agreement with the river water quality data. A MATLAB GUI platform was developed to perform numerical and graphical analysis, which can be used as a relatively simple but robust calibration tool to support model application and data analysis.

PAS1-modified optical SIS sensor for highly sensitive and specific detection of toluene.

We report on a novel solution immersed silicon (SIS) sensor modified with bio-receptor to detect toluene. To perform this approach, bio-receptor PAS1 which specifically interacts with toluene was chosen as a capture agent for SIS ellipsometric sensing. We constructed wild PAS1 and mutant PAS1 (F46A and F79Y) which are toluene binding-defective. Especially, we utilized an easily accessible capturing approach based on silica binding peptide (SBP) for direct immobilization of PAS1 on the SiO2 surfaces. After the immobilization of SBP-tagged PAS1 to the sensing layers, PAS1-based SIS sensor was evaluated for its ability to recognize toluene. As a result, a significant up-shift in Psi (Ψ) was clearly observed with a low limit of detection (LOD) of 0.1 µM, when treated with toluene on wild PAS1-surface, but not on mutant PAS1-sensing layers, indicating the selective interactions between PAS1 and toluene molecule. The PAS1-SIS sensor showed no changes in Psi (Ψ), if any, negligible, when exposed to benzene, phenol, xylene and 4-nitrophenol as negative controls, thereby demonstrating the specificity of interaction between PAS1 and toluene. Taken together, our results strongly indicate that PAS1-modified ellipsometry sensor can provide a high fidelity system for the accurate and selective detection of toluene.