Visualizing NH3 emission and the local O2 and pH microenvironment of soil upon manure application using optical sensors

Size-related variability of oxygen consumption rates in individual human hepatic cells

Accurate descriptions of the variability in single-cell oxygen consumption and its size-dependency are key to establishing more robust tissue models. By combining microfabricated devices with multiparameter identification algorithms, we demonstrate that single human hepatocytes exhibit an oxygen level-dependent consumption rate and that their maximal oxygen consumption rate is significantly lower than that of typical hepatic cell cultures. Moreover, we found that clusters of two or more cells competing for a limited oxygen supply reduced their maximal consumption rate, highlighting their ability to adapt to local resource availability and the presence of nearby cells. We used our approach to characterize the covariance of size and oxygen consumption rate within a cell population, showing that size matters, since oxygen metabolism covaries lognormally with cell size. Our study paves the way for linking the metabolic activity of single human hepatocytes to their tissue- or organ-level metabolism and describing its size-related variability through scaling laws.

Differential impacts of sewage sludge and biochar on phosphorus-related processes: An imaging study of the rhizosphere

Recycling of phosphorus (P) from waste streams in agriculture is essential to reduce the negative environmental effects of surplus P and the unsustainable mining of geological P resources. Sewage sludge (SS) is an important P source; however, several issues are associated with the handling and application of SS in agriculture. Thus, post-treatments such as pyrolysis of SS into biochar (BC) could address some of these issues. Here we elucidate how patches of SS in soil interact with the living roots of wheat and affect important P-related rhizosphere processes compared to their BC counterparts. Wheat plants were grown in rhizoboxes with sandy loam soil, and 1 cm Ø patches with either SS or BC placed 10 cm below the seed. A negative control (CK) was included. Planar optode pH sensors were used to visualize spatiotemporal pH changes during 40 days of plant growth, diffusive gradients in thin films (DGT) were applied to map labile P, and zymography was used to visualize the spatial distribution of acid (ACP) and alkaline (ALP) phosphatase activity. In addition, bulk soil measurements of available P, pH, and ACP activity were conducted. Finally, the relative abundance of bacterial P-cycling genes (phoD, phoX, phnK) was determined in the patch area rhizosphere. Labile P was only observed in the area of the SS patches, and SS further triggered root proliferation and increased the activity of ACP and ALP in interaction with the roots. In contrast, BC seemed to be inert, had no visible effect on root growth, and even reduced ACP and ALP activity in the patch area. Furthermore, there was a lower relative abundance of phoD and phnK genes in the BC rhizosphere compared to the CK. Hence, optimization of BC properties is needed to increase the short-term efficiency of BC from SS as a P fertilizer.

Selective Deionization of Thin-Layer Samples Using Tandem Carbon Nanotubes-Polymeric Membranes

Herein, we investigate the selective deionization (i.e., the removal of ions) in thin-layer samples (<100 μm in thickness) using carbon nanotubes (CNTs) covered with an ionophore-based ion-selective membrane (ISM), resulting in a CNT-ISM tandem actuator. The concept of selective deionization is based on a recent discovery by our group ( Anal. Chem. 2022, 94, 21, 7455-7459), where the activation of the CNT-ISM architecture is conceived on a mild potential step that charges the CNTs to ultimately generate the depletion of ions in a thin-layer sample. The role of the ISM is to selectively facilitate the transport of only one ion species to the CNT lattice. To estimate the deionization efficiency of such a process, a potentiometric sensor is placed less than 100 μm away from the CNT-ISM tandem, inside a microfluidic cell. This configuration helped to reveal that the selective uptake of ions increases with the capacitance of the CNTs and that the ISM requires a certain ion-exchanger capacity, but this does not further affect its efficiency. The versatility of the concept is demonstrated by comparing the selective uptake of five different ions (H+, Li+, Na+, K+, and Ca2+), suggesting the possibility to remove any cation from a sample by simply changing the ionophore in the ISM. Furthermore, ISMs based on two ionophores proved to achieve the simultaneous and selective deionization of two ion species using the same actuator. Importantly, the relative uptake between the two ions was found to be governed by the ion-ionophore binding constants, with the most strongly bound ion being favored over other ions. The CNT-ISM actuator concept is expected to contribute to the analytical sensing field in the sense that ionic interferents influencing the analytical signal can selectively be removed from samples to lower traditional limits of detection.

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.

Optode-based chemical imaging of laboratory burned soil reveals millimeter-scale heterogeneous biogeochemical responses

Soil spatial responses to fire are unclear. Using optical chemical sensing with planar 'optodes', pH and dissolved O₂ concentration were tracked spatially with a resolution of 360 μm per pixel for 72 h after burning soil in the laboratory with a butane torch (∼1300 °C) and then sprinkling water to simulate a postfire moisture event. Imaging data from planar optodes correlated with microbial activity (quantified via RNA transcripts). Post-fire and post-wetting, soil pH increased throughout the entire ∼13 cm × 17 cm × 20 cm rectangular cuboid of sandy loam soil. Dissolved O₂ concentrations were not impacted until the application of water postfire. pH and dissolved O₂ both negatively correlated (p < 0.05) with relative transcript expression for galactose metabolism, the degradation of aromatic compounds, sulfur metabolism, and narH. Additionally, dissolved O₂ negatively correlated (p < 0.05) with the relative activity of carbon fixation pathways in Bacteria and Archaea, amoA/amoB, narG, nirK, and nosZ. nifH was not detected in any samples. Only amoB and amoC correlated with depth in soil (p < 0.05). Results demonstrate that postfire soils are spatially complex on a mm scale and that using optode-based chemical imaging as a chemical navigator for RNA transcript sampling is effective.

Simultaneous visualization of flow fields and oxygen concentrations to unravel transport and metabolic processes in biological systems

From individual cells to whole organisms, O2 transport unfolds across micrometer- to millimeter-length scales and can change within milliseconds in response to fluid flows and organismal behavior. The spatiotemporal complexity of these processes makes the accurate assessment of O2 dynamics via currently available methods difficult or unreliable. Here, we present "sensPIV," a method to simultaneously measure O2 concentrations and flow fields. By tracking O2-sensitive microparticles in flow using imaging technologies that allow for instantaneous referencing, we measured O2 transport within (1) microfluidic devices, (2) sinking model aggregates, and (3) complex colony-forming corals. Through the use of sensPIV, we find that corals use ciliary movement to link zones of photosynthetic O2 production to zones of O2 consumption. SensPIV can potentially be extendable to study flow-organism interactions across many life-science and engineering applications.

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.

Recent Advances in Chemical Sensors for Soil Analysis: A Review

The continuously rising interest in chemical sensors’ applications in environmental monitoring, for soil analysis in particular, is owed to the sufficient sensitivity and selectivity of these analytical devices, their low costs, their simple measurement setups, and the possibility to perform online and in-field analyses with them. In this review the recent advances in chemical sensors for soil analysis are summarized. The working principles of chemical sensors involved in soil analysis; their benefits and drawbacks; and select applications of both the single selective sensors and multisensor systems for assessments of main plant nutrition components, pollutants, and other important soil parameters (pH, moisture content, salinity, exhaled gases, etc.) of the past two decades with a focus on the last 5 years (from 2017 to 2021) are overviewed.

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.

Silicon Cycling in Soils Revisited

Silicon (Si) speciation and availability in soils is highly important for ecosystem functioning, because Si is a beneficial element for plant growth. Si chemistry is highly complex compared to other elements in soils, because Si reaction rates are relatively slow and dependent on Si species. Consequently, we review the occurrence of different Si species in soil solution and their changes by polymerization, depolymerization, and condensation in relation to important soil processes. We show that an argumentation based on thermodynamic endmembers of Si dependent processes, as currently done, is often difficult, because some reactions such as mineral crystallization require months to years (sometimes even centuries or millennia). Furthermore, we give an overview of Si reactions in soil solution and the predominance of certain solid compounds, which is a neglected but important parameter controlling the availability, reactivity, and function of Si in soils. We further discuss the drivers of soil Si cycling and how humans interfere with these processes. The soil Si cycle is of major importance for ecosystem functioning; therefore, a deeper understanding of drivers of Si cycling (e.g., predominant speciation), human disturbances and the implication for important soil properties (water storage, nutrient availability, and micro aggregate stability) is of fundamental relevance.