The development of phosphate sensors suitable for long-term in situ deployments in natural waters, is essential to improve our understanding of the distribution, fluxes, and biogeochemical role of this key nutrient in a changing ocean. Here, we describe the optimization of the molybdenum blue method for in situ work using a lab-on-chip (LOC) analyzer and evaluate its performance in the laboratory and at two contrasting field sites. The in situ performance of the LOC sensor is evaluated using hourly time-series data from a 56-day trial in Southampton Water (UK), as well as a month-long deployment in the subtropical oligotrophic waters of Kaneohe Bay (Hawaii, USA). In Kaneohe Bay, where phosphate concentrations were characteristic of the dry season (0.13 ± 0.03 µM, n = 704), the in situ sensor accuracy was 16 ± 12% and a potential diurnal cycle in phosphate concentrations was observed. In Southampton Water, the sensor data (1.02 ± 0.40 µM, n = 1,267) were accurate to ±0.10 µM relative to discrete reference samples. Hourly in situ monitoring revealed striking tidal and storm derived fluctuations in phosphate concentrations in Southampton Water that would not have been captured via discrete sampling. We show the impact of storms on phosphate concentrations in Southampton Water is modulated by the spring-neap tidal cycle and that the 10-fold decline in phosphate concentrations observed during the later stages of the deployment was consistent with the timing of a spring phytoplankton bloom in the English Channel. Under controlled laboratory conditions in a 250 L tank, the sensor demonstrated an accuracy and precision better than 10% irrespective of the salinity (0-30), turbidity (0-100 NTU), colored dissolved organic matter (CDOM) concentration (0-10 mg/L), and temperature Grand et al.In situ Lab-On-Chip Phosphate Sensor (5-20 • C) of the water (0.3-13 µM phosphate) being analyzed. This work demonstrates that the LOC technology is mature enough to quantify the influence of stochastic events on nutrient budgets and to elucidate the role of phosphate in regulating phytoplankton productivity and community composition in estuarine and coastal regimes.

The identification of submillimetre phytoplankton is important for monitoring environmental and climate changes, as well as evaluating water for health reasons. Current standard methods for phytoplankton species identification require sample collection and ex situ analysis, an expensive procedure which prevents the rapid identification of phytoplankton outbreaks. To address this, we use a glass-based microchip with a microchannel and waveguide included on a monolithic substrate, and demonstrate its use for identifying phytoplankton species. The microchannel and the specimens inside it are illuminated by laser light from the curved waveguide as algae-laden water is passed through the channel. The intensity distribution of the light collected from the biochip is monitored with an external photodetector. Here, we demonstrate that the characteristics of the photodiode signal from this simple and robust system can provide significant and useful information as to the contents of the channel. Specifically, we show first that the signals are correlated to the size of algae cells. Using a pattern-matching neural network, we demonstrate the successful classification of five algae species with an average 78% positive identification rate. Furthermore, as a proof-of-concept for field-operation, we show that the chip can be used to distinguish between detritus in field-collected water and the toxin-producing cyanobacterium Cyanothece.

The rapid identification of algae species is not only of practical importance when monitoring unwanted adverse effects such as eutrophication, but also when assessing the water quality of watersheds. Here, we demonstrate a lab-on-a-chip that functions as a compact robust tool for the fast screening, real-time monitoring, and initial classification of algae. The water-algae sample, flowing in a microfluidic channel, is side-illuminated by an integrated subsurface waveguide. The waveguide is curved to improve the device sensitivity. The changes in the transmitted optical signal are monitored using a quadrant-cell photo-detector. The signal-wavelets from the different quadrants are used to qualitatively distinguish different families of algae. The channel and waveguide are fabricated out of a monolithic fused-silica substrate using a femtosecond laser-writing process combined with chemical etching. This proof-of-concept device paves the way for more elaborate femtosecond laser-based optofluidic micro-instruments incorporating waveguide networks designed for the real-time field analysis of cells and microorganisms.

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simplicity of inertial microfluidics make it appropriate for the high-throughput pre-sorting of algae cells upstream of other integrated sensing modalities in a field-deployable device.