Cable Bacteria Skeletons as Catalytically Active Electrodes.

Cable bacteria are multicellular, filamentous bacteria that use internal conductive fibers to transfer electrons over centimeter distances from donors within anoxic sediment layers to oxygen at the surface. We extracted the fibers and used them as free-standing bio-based electrodes to investigate their electrocatalytic behavior. The fibers catalyzed the reversible interconversion of oxygen and water, and an electric current was running through the fibers even when the potential difference was generated solely by a gradient of oxygen concentration. Oxygen reduction as well as oxygen evolution were confirmed by optical measurements. Within living cable bacteria, oxygen reduction by direct electrocatalysis on the fibers and not by membrane-bound proteins readily explains exceptionally high cell-specific oxygen consumption rates observed in the oxic zone, while electrocatalytic water oxidation may provide oxygen to cells in the anoxic zone.

Optical sensors (optodes) for multiparameter chemical imaging: classification, challenges, and prospects.

Chemical gradients and uneven distribution of analytes are common in natural and artificial systems. As a result, the ability to visualize chemical distributions in two or more dimensions has gained significant importance in recent years. This has led to the integration of chemical imaging techniques into all domains of analytical chemistry. In this review, we focus on the use of optical sensors, so-called optodes, to obtain real-time and multidimensional images of two or more parameters simultaneously. It is important to emphasize that multiparameter imaging in this context is not confined solely to multiple chemical parameters (analytes) but also encompasses physical (e.g., temperature or flow) or biological (e.g., metabolic activity) parameters. First, we discuss the technological milestones that have paved the way for chemical imaging using optodes. Later, we delve into various strategies that can be taken to enable multiparameter imaging. The latter spans from developing novel receptors that enable the recognition of multiple parameters to chemometrics and machine learning-based techniques for data analysis. We also explore ongoing trends, challenges, and prospects for future developments in this field. Optode-based multiparameter imaging is a rapidly expanding field that is being fueled by cutting-edge technologies. Chemical imaging possesses the potential to provide novel insights into complex samples, bridging not only across various scientific disciplines but also between research and society.

Timing matters: the overlooked issue of response time mismatch in pH-dependent analyte sensing using multiple sensors.

Accurate measurement of pH-dependent analytes is crucial for a wide range of applications, including environmental monitoring, industrial processes, and healthcare diagnostics. In multi-sensor systems, combining data from multiple sensors offers the potential for more comprehensive analysis, yet it is important to be aware of the limitations of this approach. In this paper, we investigate the often-overlooked issue of response time mismatch among sensors, which can introduce significant errors in calculated sum parameters. We present a model and software application (SensinSilico) that allows predicting the error arising from a mismatch of sensor response times. The model was compared and validated using experimental results from calculations of total dissolved sulphide (TDS). These calculations were based on data from concurrent sensor measurements of hydrogen sulfide (H2S) and pH, which had different response times. We believe that SensinSilico has the potential to be a powerful tool for researchers, professionals, and end-users, enabling them to estimate and minimize errors arising from response time mismatches, enhancing the accuracy and reliability of their results.

Machine learning for optical chemical multi-analyte imaging : Why we should dare and why it's not without risks.

Simultaneous sensing of metabolic analytes such as pH and O2 is critical in complex and heterogeneous biological environments where analytes often are interrelated. However, measuring all target analytes at the same time and position is often challenging. A major challenge preventing further progress occurs when sensor signals cannot be directly correlated to analyte concentrations due to additional effects, overshadowing and complicating the actual correlations. In fields related to optical sensing, machine learning has already shown its potential to overcome these challenges by solving nested and multidimensional correlations. Hence, we want to apply machine learning models to fluorescence-based optical chemical sensors to facilitate simultaneous imaging of multiple analytes in 2D. We present a proof-of-concept approach for simultaneous imaging of pH and dissolved O2 using an optical chemical sensor, a hyperspectral camera for image acquisition, and a multi-layered machine learning model based on a decision tree algorithm (XGBoost) for data analysis. Our model predicts dissolved O2 and pH with a mean absolute error of < 4.50·10-2 and < 1.96·10-1, respectively, and a root mean square error of < 2.12·10-1 and < 4.42·10-1, respectively. Besides the model-building process, we discuss the potentials of machine learning for optical chemical sensing, especially regarding multi-analyte imaging, and highlight risks of bias that can arise in machine learning-based data analysis.

Noise versus Resolution in Optical Chemical Imaging-How Reliable Are Our Measurements?

Optical chemical imaging has established itself as a valuable technique for visualizing analyte distributions in 2D, notably in medical, biological, and environmental applications. In particular for image acquisitions on small scales between few millimeter to the micrometer range, as well as in heterogeneous samples with steep analyte gradients, image resolution is essential. When individual pixels are inspected, however, image noise becomes a metric as relevant as image accuracy and precision, and denoising filters are applied to preserve relevant information. While denoising filters smooth the image noise, they can also lead to a loss of spatial resolution and thus to a loss of relevant information about analyte distributions. To investigate the trade-off between image resolution and noise reduction for information preservation, we studied the impact of random camera noise and noise due to incorrect camera settings on oxygen optodes using the ratiometric imaging technique. First, we estimated the noise amplification across the calibration process using a Monte Carlo simulation for nonlinear fit models. We demonstrated how initially marginal random camera noise results in a significant standard deviation (SD) for oxygen concentration of up to 2.73% air under anoxic conditions, although the measurement was conducted under ideal conditions and over 270 thousand sample pixels were considered during calibration. Second, we studied the effect of the Gaussian denoising filter on a steep oxygen gradient and investigated the impact when the smoothing filter is applied during data processing. Finally, we demonstrated the effectiveness of a Savitzky-Golay filter compared to the well-established Gaussian filter.

Optode Based Chemical Imaging-Possibilities, Challenges, and New Avenues in Multidimensional Optical Sensing.

Seeing is believing, as the saying goes, and optical sensors (so-called optodes) are tools that can make chemistry visible. Optodes react reversibly and quickly (seconds to minutes) to changing analyte concentrations, enabling the spatial and temporal visualization of an analyte in complex environments. By being available as planar sensor foils or in the form of nano- or microparticles, optodes are flexible tools suitable for a wide array of applications. The steadily grown applications of in particular oxygen (O2) and pH optodes in fields as diverse as medical, environmental, or material sciences is proof for the large demand of optode based chemical imaging. Nevertheless, the full potential of this technology is not exhausted yet, challenges have to be overcome, and new avenues wait to be taken. Within this Perspective, we look at where the field currently stands, highlight several successful examples of optode based chemical imaging and ask what it will take to advance current state-of-the-art technology. It is our intention to point toward some potential blind spots and to inspire further developments.

Dynamic Sensor Concept Combining Electrochemical pH Manipulation and Optical Sensing of Buffer Capacity.

State-of-the-art electrochemical and optical sensors present distinct advantages and disadvantages when used individually. Combining both methodologies offers interesting synergies and makes it possible to exploit strengths and circumvent possible problems of the individual methods. We report a dynamic sensing concept for buffer capacity by applying water electrolysis to modulate the pH microenvironment in front of an optical pH sensor placed in a flow cell. Using this combinatory approach in a nonequilibrium readout mode allowed us to assess the concentration of different buffer species in relatively short time (1 min per measurement). Theoretical simulations of the system were performed to validate the presented method. Additionally, the dynamic measurement approach enabled in situ determination of the apparent pKa of MOPS (3-(N-morpholino)propanesulfonic acid) buffer at ambient conditions. The dynamic and combinatory approach presented here holds large potential also for other pH-active analytes.

Hyperspectral Luminescence Imaging in Combination with Signal Deconvolution Enables Reliable Multi-Indicator-Based Chemical Sensing.

Although real-time monitoring of individual analytes using reversible optical chemical sensors (optodes) is well established, it remains a challenge in optical sensing to monitor multiple analyte concentrations simultaneously. Here, we present a novel sensing approach using hyperspectral imaging in combination with signal deconvolution of overlapping emission spectra of multiple luminescent indicator dyes, which facilitates multi-indicator-based chemical imaging. The deconvolution algorithm uses a linear combination model to describe the superimposed sensor signals and employs a sequential least-squares fit to determine the percent contribution of the individual indicator dyes to the total measured signal. As a proof of concept, we used the algorithm to analyze the measured response of an O2 sensor composed of red-emitting Pd(II)/Pt(II) porphyrins and NIR-emitting Pd(II)/Pt(II) benzoporphyrins with different sensitivities. This facilitated chemical imaging of O2 over a wide dynamic range (0-950 hPa) with a hyperspectral camera system (470-900 nm). The applicability of the novel method was demonstrated by imaging the O2 distribution in the heterogeneous microenvironment around the roots of the aquatic plant Littorella uniflora. The presented approach of combining hyperspectral sensing with signal deconvolution is flexible and can easily be adapted for use of various multi-indicator- or even multianalyte-based optical sensors with different spectral characteristics, enabling high-resolution simultaneous imaging of multiple analytes.

TADF-Emitting Zn(II)-Benzoporphyrin: An Indicator for Simultaneous Sensing of Oxygen and Temperature.

A new luminescent indicator is presented that enables simultaneous measurement of oxygen and temperature at a single wavelength. The indicator, an alkylsulfone-substituted Zn(II)-meso-tetraphenyltetrabenzoporphyrin, emits prompt and thermally activated delayed fluorescence (TADF). TADF is sensitive toward oxygen and temperature and is referenced against prompt fluorescence (PF) that is not affected by oxygen. The information on both parameters is accessed from the decay time of TADF and the temperature-dependent ratio of TADF and PF. Sensor foils, made from poly(styrene-co-acrylonitrile) and the indicator dye, enable temperature-compensated trace oxygen sensing (0.002-6 hPa pO2) at ambient conditions. Compared to the previously reported dual sensors based on two emitters, the new sensor significantly simplifies the experimental setup and eliminates risks of different leaching or photobleaching rates by utilizing only one indicator dye and operating at a single wavelength.

Spectral Characterization of Eight Marine Phytoplankton Phyla and Assessing a Pigment-Based Taxonomic Discriminant Analysis for the in Situ Classification of Phytoplankton Blooms.

Early stage identification of harmful algal blooms (HABs) has gained significance for marine monitoring systems over the years. Various approaches for in situ classification have been developed. Among them, pigment-based taxonomic classification is one promising technique for in situ characterization of bloom compositions, although it is yet underutilized in marine monitoring programs. To demonstrate the applicability and importance of this powerful approach for monitoring programs, we combined an ultra low-cost and miniaturized multichannel fluorometer with Fisher's linear discriminant analysis (LDA). This enables the real-time characterization of algal blooms at order level based on their spectral properties. The classification capability of the algorithm was examined with a leave-one-out cross validation of 53 different unialgal cultures conducted in terms of standard statistical measures and independent figures of merit. The separation capability of the linear discriminant analysis was further successfully examined in mixed algal suspensions. Besides this, the impact of the growing status on the classification capability was assessed. Further, we provide a comprehensive study of spectral features of eight different phytoplankton phyla including an extensive study of fluorescence excitation spectra and marker pigments analyzed via HPLC. The analyzed phytoplankton species belong to the phyla of Cyanobacteria, Dinophyta (Dinoflagellates), Bacillariophyta (Diatoms), Haptophyta, Chlorophyta, Ochrophyta, Cryptophyta, and Euglenophyta.

Compact and Low-Cost Fluorescence Based Flow-Through Analyzer for Early-Stage Classification of Potentially Toxic Algae and in Situ Semiquantification.

The occurrence and intensity of (harmful) algal blooms (HABs) have increased through the years due to rapidly changing environmental conditions. At the same time, the demand for low-cost instrumentation has increased substantially, enabling the real-time monitoring and early-stage detection of HABs. To meet this challenge, we have developed a compact multi-wavelength fluorometer for less than 400 USD. This is possible by using readily available and low-cost optical and electronic components. Its modular design results in a highly versatile and flexible monitoring tool. The algae detection module enables a continuous identification and control of relevant algal groups based on their spectral characteristics with a detection limit of 10 cells per liter. Besides its usage as a benchtop module in the laboratory, the algae module has been integrated into submersible housings and applied in coastal environments. During its first in situ application in the Port of Genoa, seawater samples of mixed algal composition were used to demonstrate the successful discrimination of cyanobacteria and dinophytes as well-known toxin producing classes. Fabrication, operation, and performance as well as its first in situ application are addressed.

SCHeMA EU Project Summer School Report (Bilbao June 16 - 17, 2016).

This conference report describes the training activities that took place in the frame of the Integrated in Situ Chemical MApping probe (SCHeMA) summer school organized from the 14th to the 16th of June 2016 in Bilbao (Spain).

Fast pesticide detection inside microfluidic device with integrated optical pH, oxygen sensors and algal fluorescence.

The necessities of developing fast, portable, cheap and easy to handle pesticide detection platforms are getting attention of scientific and industrial communities. Although there are some approaches to develop microchip based pesticide detection platforms, there is no compact microfluidic device for the complementary, fast, cheap, reusable and reliable analysis of different pesticides. In this work, a microfluidic device is developed for in-situ analysis of pesticide concentration detected via metabolism/photosynthesis of Chlamydomonas reinhardtii algal cells (algae) in tap water. Algae are grown in glass based microfluidic chip, which contains integrated optical pH and oxygen sensors in a portable system for on-site detection. In addition, intrinsic algal fluorescence is detected to analyze the pesticide concentration in parallel to pH and oxygen sensors with integrated fluorescence detectors. The response of the algae under the effect of different concentrations of pesticides is evaluated and complementary inhibition effects depending on the pesticide concentration are demonstrated. The three different sensors allow the determination of various pesticide concentrations in the nanomolar concentration range. The miniaturized system provides the fast quantification of pesticides in less than 10min and enables the study of toxic effects of different pesticides on Chlamydomonas reinhardtii green algae. Consequently, the microfluidic device described here provides fast and complementary detection of different pesticides with algae in a novel glass based microfluidic device with integrated optical pH, oxygen sensors and algal fluorescence.