# Low-cost CMOS-based luminescence lifetime imaging with oxygen, temperature and pH sensors

Gauthier Rousseau, Marianne Pons, Hessel Adelerhof, Nathan Pellerin, Mart Giesbergen, Bastien Carde, Martin Wolff, Koen Blanckaert, Sergey M Borisov, Benoit Fond

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Gauthier Rousseau, Marianne Pons, Hessel Adelerhof, Nathan Pellerin, Mart Giesbergen, et al., Low-cost CMOS-based luminescence lifetime imaging with oxygen, temperature and pH sensors. Sensors and Actuators B: Chemical, 2026, 447 Part 1, pp.138849. 10.1016/j.snb.2025.138849. hal-05385465

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# Low-cost CMOS-based luminescence lifetime imaging with oxygen, temperature and pH sensors

Gauthier Rousseau<sup>a,●</sup>,*, Marianne Pons<sup>a,●</sup>, Hessel Adelerhof<sup>a</sup>, Nathan Pellerin<sup>a,●</sup>, Mart Giesbergen<sup>a</sup>, Bastien Carde<sup>a</sup>, Martin Wolff<sup>a</sup>, Koen Blanckaert<sup>a,●</sup>, Sergey M. Borisov<sup>b,●</sup>,*, Benoit Fond<sup>c,*</sup>

## ARTICLE INFO

Dataset link: https://doi.org/10.5281/zenodo.15662176, https://gitlab.com/groussea/clicc, https://tucloud.tuwien.ac.at/index.php/s/kWNqpmCoppYDyL

**Keywords:**
Phosphorescence
Fluorescence
TADF
Decay time
Camera

## ABSTRACT

Luminescence imaging enables spatiotemporal visualization of physical and chemical quantities, yet widespread adoption has been hindered by expensive, specialized instrumentation. Here, we present a compact platform that employs a consumer CMOS camera for versatile luminescence imaging across biomedical research, geosciences, and aerospace engineering applications. Our approach exploits the on-chip charge accumulation capability to perform multiple exposures in one frame, thereby combining the robustness of lifetime-based measurements with the superior signal quality of time-integrated detection. We demonstrate platform versatility through three applications: (i) oxygen mapping using phosphorescent platinum(II) benzoporphyrin optodes; (ii) the first demonstration of temperature mapping in fluid using thermally-activated delayed fluorescence (TADF) nanoparticles in a laser plane, achieving ±1 °C (1σ) precision for single-shot single-pixel at a 500 µm spatial resolution and 7 Hz temporal resolution; and (iii) pH mapping through dual lifetime referencing using a combination of a fluorescent aza-BODIPY indicator and phosphorescent Egyptian blue reference material. With total costs of approximately €2,000 for the LED configuration and €4,000 for the laser configuration, our platform reduces instrumentation barriers while maintaining precision and sampling rates comparable to specialized systems. We provide open-source Python and Arduino hardware control code (Clicc - Cumulative Lifetime Imaging with CMOS-Camera) to ensure reproducibility and encourage adoption.

## 1. Introduction

Luminescence-based sensing has emerged as an essential analytical tool over recent decades, offering the unique advantage of visualizing distributions of chemical and physical quantities in two and three dimensions [1,2]. While point-measurement techniques such as microelectrodes and microoptodes remain viable for larger objects, luminescence imaging provides comprehensive spatial information that reveals gradients of quantities across entire samples.

This approach has been widely adopted for visualizing various parameters, including oxygen [3–5], pH [6], calcium [7], and temperature [8]. In geoscience applications, it has enhanced understanding of sediment processes [9,10] and groundwater interactions [11]. In biomedical contexts, it has been used for wound healing monitoring [12] and can serve as a tool to improve cancer diagnostics [13]. On a larger scale, imaging of local pressure (via oxygen quenching) or temperature on aerodynamic surfaces in wind tunnels plays a central

role in the development of more efficient vehicles [14,15]. Luminescent dyes for temperature mapping can be integrated with particle image velocimetry (PIV) [16], while luminescent particles themselves can serve as flow tracers for velocity measurements [17]. Advanced techniques like SensPIV [18] and thermographic PIV [19] have further expanded capabilities by simultaneously capturing both scalar and velocity fields, providing comprehensive data for understanding complex reactive mixing and thermal processes.

Despite numerous reports on luminescence imaging, its widespread adoption in various communities has been hindered by (i) sensing materials availability and (ii) compact, resource-efficient and reliable instrumentation. Whereas progress has been achieved in the first area, particularly regarding materials for sensing and imaging of oxygen [20], pH [6] and temperature [21], the respective instrumentation necessary for robust imaging remains a bottleneck. Self-referenced methods based

<footnote><sup>a</sup> Institute of Hydraulic Engineering and Water Resources Management, TU Wien, Karlsplatz 13, 1040, Vienna, Austria</footnote>

<footnote><sup>b</sup> Institute of Analytical Chemistry and Food Chemistry, Graz University of Technology, Stremayrgasse 9, 8010 Graz, Austria</footnote>

<footnote><sup>c</sup> ONERA, The French Aerospace Lab, Department of Aerodynamics, Aeroelasticity and Aeroacoustics (DAAA), Paris-Saclay University, Meudon, 92190, France</footnote>

<footnote>* Corresponding authors.
E-mail addresses: gauthier.rousseau@tuwien.ac.at (G. Rousseau), sergey.borisov@tugraz.at (S.M. Borisov), benoit.fond@onera.fr (B. Fond).</footnote>
on acquiring temporally (luminescence lifetime imaging) or spectrally separated (color imaging) signals are essential to avoid the influence of illumination or concentration inhomogeneities on absolute signals. Spectral distortions in the optical path [22] are more likely than temporal distortions on microsecond timescales, so that lifetime imaging is in principle favored. Many early studies on oxygen and pH dynamics employed rapid lifetime determination (RLD) or dual lifetime referencing (DLR) using the now-discontinued interline transfer charge-coupled device (CCD) cameras [23], which allow for time-gated on chip accumulation and are still in use in some groups. These cameras are also frequently used in 'PIV mode' for single pulse lifetime imaging, where frames separated by a short interframe time straddle the excitation pulse—a technique known as 'frame straddling'[24,25]. A specialized camera from PCO (a scientific camera manufacturer) enables FLIM (Fluorescence Lifetime Imaging Microscopy), allowing imaging of quantities such as oxygen or temperature via luminescence lifetime using double charge taps [26]. However, it has limitations that include the interference from background fluorescence due to frequency domain read-out, inability to measure decay times above 100 µs, and high price. Therefore, many groups utilized simpler and more affordable instrumentation, such as consumer 3-channel red-green-blue (RGB) cameras [9,27,28], including more expensive commercial versions that can be purchased in combination with dedicated optical sensing materials [13]. Accurate imaging with RGB cameras requires the addition of a reference material (whose luminescence is independent on the measured quantity) to enable ratiometric measurements. Such reference is often difficult to find since it should show similar excitation characteristics but emission significantly different from that of the indicator, and it introduces additional risks of drift due to leaching and photobleaching. Moreover, the materials remain affected by background fluorescence and wavelength-dependent scattering.

The instrumentation bottleneck has been recognized by other groups, and very recently new imaging approaches that rely on single complementary metal-oxide-semiconductor (CMOS) cameras for lifetime imaging were presented. Using a frame straddling strategy on two consecutive pulses, inexpensive global shutter cameras with relatively long interframe time were used for oxygen imaging [29]. The second study uses CMOS camera, with periodic shutter activation, to capture 11 portions of the luminescence decay in an accumulation strategy and derive lifetime maps for glucose sensing [30].

In this work we report an imaging platform that relies on two time-gated on-chip accumulation read-out of a CMOS Camera that enables self-referenced imaging of a variety of physical and chemical quantities. Platform versatility is demonstrated through three applications: oxygen phosphorescence lifetime imaging using planar optodes, temperature mapping in fluid flows using thermally-activated delayed fluorescence (TADF) nanoparticles within a laser plane — representing the first demonstration of TADF-based 2D temperature imaging with a temporal resolution of 7 Hz and a spatial resolution of 500 µm — and pH imaging via DLR. To encourage rapid adoption and ensure reproducibility, we provide open-source hardware control code called Cumulative Lifetime Imaging with CMOS-Camera (Clicc), implemented in Python and Arduino available at https://gitlab.com/groussea/clicc.

## 2. Experimental section

### 2.1. Instrumentation

The imaging platform employs a monochrome Ximea camera with a SONY Pregius IMX250 monochrome CMOS sensor (5.0 MP, global shutter) in a USB3.1 configuration (Ximea reference MC050MG-SY-UB) synchronized with either a high-power pulsed light-emitting diode (LED) array or a diode laser excitation source. Timing control is provided by an Arduino R3 microcontroller programmed using Arduino and Python languages using the open source Clicc code. The microcontroller triggers the CMOS image sensor via a square wave signal through

the camera's trigger input. The Arduino's clock limits the minimum pulse duration to approximately 1 µs, which is below both the camera's minimum exposure time (15 µs) and the minimum modulation period (100 µs). Image data transfers directly from the camera to the computer via USB for each frame, with a maximum frame rate of 74 frames per second at full resolution. The camera features a critical capability that enables our approach: multiple exposures in one frame, which we exploit to develop a time-gated and accumulated referenced acquisition strategy.

The system used for oxygen and pH imaging experiments is shown in Fig. 1a. In this configuration, we employed a C-mount Cosmicar 16 mm 1:1.4 television lens and a pulsed red LED (round array of 12x high power OSLON LEDs from OSRAM equipped with a PMMA lens array and aluminium heatsink) with a custom-built LED trigger to illuminate PET foils coated with specific indicators in contact with sample solutions or gas mixtures. A near-infrared (NIR) long-pass filter RG 9 (Schott) isolates the emission of the luminophores.

The temperature imaging configuration (Fig. 1b) utilized a pulsed laser sheet to illuminate a quartz cuvette (Hellma, dimensions: 5 × 1 ×5cm³, wall thickness: 2 mm) containing temperature-sensitive Zr(MesIPDPt-BuPh)₂ nanoparticles (1 mg mL⁻¹). Imaging was achieved using a Ricoh C-Mount lens (6 mm focal length) combined with a Midopt LP550-46 orange long-pass filter. Illumination was provided by a multimode diode laser emitting at λ = 532 nm (Viasho 532 nm, P = 4 W output power) generating a laser sheet through a Powell lens. The laser sheet thickness is estimated at w ~ 1 mm and height at h ~ 10 cm, providing a power density of approximately 80 kW/m². Total system costs are approximately €2,000 for the LED configuration and €4,000 for the laser configuration (see Table S1 and Table S2 of component prices bought in 2023 for the 2 platforms in Supporting Information).

### 2.2. Imaging methods

Luminescence lifetime imaging is performed using two complementary methods: (1) RLD for oxygen and temperature sensing, and (2) DLR for pH detection. Both approaches exploit the camera's multiple exposures' capability to accumulate signal from multiple excitation-emission cycles within each frame, significantly improving signal-to-noise ratios while maintaining temporal resolution.

#### 2.2.1. Rapid lifetime determination (RLD)

RLD method is a family of analytical equations commonly used to extract exponential lifetime from a limited number of time integrated photon fluxes, instead of recording complete decay curves and traditional least-squares analysis [31,32]. The periods, window 1 (W1) and window 2 (W2), are captured with different delays from the end of the excitation pulse. The decay time constant can then be derived from the ratio of the two windows R = W2/W1 assuming a mono or double-exponential decay. When luminescence responds linearly, the ratio method helps to eliminate signal variations caused by heterogeneous illumination or concentration. Additionally, it is a self-referenced method since this elimination is performed using a single luminescent material. For a single-exponential decay and equal successive window exposure duration Δt for the two windows, the lifetime τ is calculated from

$$ \tau = - \frac{\Delta t}{\log(R)} \qquad (1) $$

Rather than capturing a single decay curve, we accumulate signal from multiple excitation-emission cycles within each frame [30,33,34]. The signal-to-noise ratio (SNR) increases with the number of exposures Nexp with a set excitation pulse-window delay. We depict the temporal acquisition strategy and signal processing workflow for accumulated RLD measurements in Fig. 2. The sampling rate per seconds (fps = 1/ttot, where ttot is the total time to capture the two images (Window 1 and Window 2) and obtain one final image) is determined by the total
<figure><img src="image_4.png" /><figcaption>Fig. 1. Platform configurations for luminescence lifetime imaging: (a) system for oxygen and pH measurements using a pulsed red LED to illuminate a PET foil coated with sensor materials in contact with the sample solution. (b) Temperature measurement system utilizing a time modulated diode laser sheet to illuminate a cuvette containing temperature-sensitive nanoparticles. Both set-ups employ an electronic sequencer (Arduino R3) to synchronize the CMOS camera and illumination source. RLD is used for oxygen and temperature measurements, while DLR is employed for pH sensing. Appropriate optical filters (NIR long-pass for oxygen/pH; orange long-pass for temperature) are mounted on the camera for selective detection.</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>Fig. 2. Accumulation and rapid lifetime methodology: timing scheme of the dual frame ratio principle showing excitation pulses, luminescence decay, and camera operation cycles with two measurement windows. Multiple exposures ($N_{exp}$) are accumulated for each window to improve signal quality. The bottom row displays the resulting Window 1 and Window 2 intensity images, their ratio ($R = Window2/Window1$), and the calculated lifetime map ($\tau = -\Delta t/\log R$, where $\Delta t$ is the time window duration). The workflow is illustrated for oxygen sensing experiments in which the sensor foil responds to a nitrogen jet with increased lifetime as oxygen concentration decreases.</figcaption></figure>

time needed to complete both multiple exposures windowed images plus the two additional times required for the charge read-out ($t_{ro}$).

$$t_{tot} = 1/fps_{theo} = 2(N_{exp}(t_{on} + t_{off}) + t_{ro}), \quad (2)$$

where $t_{on}$ is the time the excitation source is on and $t_{off}$ is the time it is off between two successive excitation pulses. Increasing the number of exposures improves the SNR but consequently decreases the theoretical sampling rate ($fps_{theo}$).

The timing parameters of the accumulation technique are bounded by the camera sensor's capabilities. The window exposure duration $\Delta t$ cannot be set below the hardware minimum ($\Delta t_{min} = 15$ µs for our Sony IMX 250 sensor), and the sampling rate $fps_{theo}$ is bounded by half the maximum frame rate (since two images are needed per measurement), yielding $fps_{theo,max} = 37$ Hz at full resolution. Hardware binning or cropping can increase frame rates. In practice, the images are acquired through Python code with additional processes between captures, making the acquisition slightly longer. Therefore, we determine the effective sampling rates ($fps_{eff}$) a posteriori from the timestamps of the captured images. While the effective sampling rate could approach $fps_{theo,max} = 37$ Hz, our applications were always significantly lower (below 12 Hz) due to light intensity constraints. To achieve satisfactory SNR, we needed to accumulate over $N_{exp}$ exposures, which reduced the achievable frame rates below $fps_{theo,max}$.

To ensure that any additional light in the room does not affect the results, a background image without excitation light is taken before each recording with same gating scheme and subtracted to their respective window images.

Once $\tau$ is determined using RLD, or the ratio, they must be converted to temperature or oxygen concentration using the experimentally established relationship between ratios/decay times and various known temperatures or oxygen concentrations. Measured $\tau$ with RLD can also be compared to independent lifetime measurements made in the time domain to verify the consistency of the methodology, as demonstrated in the Results section. Our method employed constant window durations ($\Delta t$) primarily to facilitate straightforward lifetime retrieval using RLD strategy, but the gating (windowing) scheme presents significant optimization potential through independent refinement of both exposure window and illumination durations. A table of timings (Table S3) employed for the RLD experiments is provided in Supporting Information.

### 2.2.2. Dual lifetime referencing (DLR)

Similarly to RLD, DLR imaging involves capturing two distinct images to generate a ratio. However, in DLR, one image is acquired during the excitation period when the light source is on ($W_{ex}$), and the other during the decay period when the light source is off ($W_{em}$). The $W_{em}$ measurements are collected after an appropriate delay to eliminate the short-lived fluorescence from the indicator and are exclusively
determined by quantity-independent long-lived phosphorescence of the reference material. The Wex image captures the combined intensities of both short-lived and long-lived luminescence components. By performing this ratio of intensities, the measurements are made independent of the excitation power and concentration. Further details on this methodology can be found in previous studies [35,36]. The windowing scheme for DLR is depicted in Figure S1 in Supporting Information.

By leveraging the multiple exposure in one frame capability of our CMOS camera, we can, as done for RLD, accumulate multiple DLR phosphorescence decays to achieve reliable SNR values. Although the RLD scheme allows determination of τ, DLR is only intended to create a ratio, in this case, dependent on pH. The relation between the Ratio and the pH is established through a calibration. A table of timings (Table S4) employed for the DLR experiment is provided in Supporting Information.

## 2.3. Luminescent materials

### 2.3.1. Oxygen-sensitive planar foil

Platinum(II) tetra(4-fluorophenyl)tetrabenzoporphyrin (=PtTPTBPF) [37] was prepared via the template method [38], analytical data provided previously in [39]. This indicator is nowadays commercially available from different suppliers (Frontier Specialty chemicals, Porphyrin Systems, etc.; CAS: 1092095-10-7). Polystyrene (=PS, average M.W. 260 000 Da) was obtained from Acros Organics. Poly(ethylene terephthalate) (PET) and polyethylene naphthalate (PEN) supports were from Pütz (Taunusstein, Germany).

Planar foil (or optode) for oxygen imaging was prepared by dissolving PtTPTBPF (1.5 mg) and PS (100 mg) in 1100 mg chloroform (VWR) and coating the obtained solution onto the PET foil with help of a bar applicator (25 µm wet film thickness) from BYK (BYK-Gardner GmbH, Geretsried, Germany). Faster responding foils for dynamic experiments in the flow-through cell were prepared by additionally adding 100 mg of microcrystalline (1–2 µm) diamond (Pureon AG, Lengwil Switzerland) per 100 mg of PS and coating the obtained ‘cocktail’ onto a PEN foil with a bar applicator (12 µm wet film thickness).

Luminescence decays (Figure S2, Supporting Information) for the oxygen planar foil were acquired on a Fluorolog 3 spectrofluorometer from Horiba Scientific equipped with a DeltaHub module, a SpectraLED excitation source (λ = 456 nm) from Horiba and a R2658 PMT from Hamamatsu. The luminescence decays were acquired in the maximum of the emission spectrum (765 nm). For the measurements, the foil was fixed in a home-made flow-through cell.

For quantitative oxygen sensing, we employed the modified Stern–Volmer Eq. (3) derived from the ‘two-site model’ developed by Demas and co-workers [40]:

$$ \frac{\tau}{\tau_0} = \frac{f}{1 + K_{SV1} pO_2} + \frac{1 - f}{1 + K_{SV2} pO_2} \quad (3) $$

where KSV1 and KSV2 represent the Stern–Volmer constants for the first and second sites, respectively, and f denotes the distribution coefficient. Fitting this equation on our data yielded very low values of KSV2, in the following we assume KSV2 = 0 to simplify the inversion of Eq. (3) to obtain pO2 values.

### 2.3.2. Nanoparticle dispersion for fluid thermometry

Nanoparticles for temperature imaging were obtained by incorporation of the temperature indicator Zr(MesIPDPt-BuPh)2 [5] into poly(vinylidene chloride-co-acrylonitrile-co-methacrylic acid) (=PVA-MAA) nanoparticles (Ø ~ 35 nm) [5] via nanoprecipitation technique. Briefly, 0.5 mg of the indicator and 50 mg of the polymer were dissolved in 25 mL of the mixture of acetone and tetrahydrofuran (9:1 v/v) and 100 mL of water were added rapidly (2–3 s) under vigorous stirring. The organic solvents were removed under reduced pressure at 40 °C. For the imaging experiments the aqueous dispersion contained 1 mg mL-1 of nanoparticles. Luminescence decays (Figure

S3 in Supporting Information) were acquired similarly to the oxygen planar foil by detecting in the maximum of the emission spectrum of the temperature indicator (591 nm).

The temperature-dependent lifetime response was fit using an Arrhenius-type model [41]:

$$ \tau = \left( k_0 + k_1 \times e^{-\frac{dE_{S1-T1}}{k_B T}} \right)^{-1} \quad (4) $$

where k0 is the temperature-independent decay rate for the excited-state deactivation, k1 is a pre-exponential factor, kB = 8.617 10-5 eV/K is the Boltzmann constant, dE$_{S1}$$_{−}$$_{T1}$ is the energy gap between the first triplet and first singlet state, and T is the absolute temperature.

### 2.3.3. pH-sensitive planar foil

First, 0.5 mg of the aza-BODIPY dye (3-chloro-4-hydroxyphenyl and 4-butoxyphenyl substituents, dye 4) [42], 66 mg of trimethylsilyl-modified Egyptian blue microparticles [43] and 200 mg of HydroMed D4 (AdvanSource Biomaterials) were dissolved/dispersed in 1.8 g of tetrahydrofuran. The resulted ‘cocktail’ was coated onto the PET foil with a bar applicator (75 µm wet film thickness). The buffers for pH calibration included acetic acid (pH 4 and 5), 2-(N-morpholino)ethane sulfonic acid (pH 5.5), 3-(N-morpholino) propanesulfonic acid (pH 7, 7.5 and 8) and N-cyclohexyl-2-aminoethanesulfonic acid (pH 9, 10) used in the concentration of 50 mM along with 100 mM of sodium chloride.

We modeled the Ratio obtained with the DLR method with a typical sigmoidal curve function:

$$ R(pH) = \frac{L}{1 + e^{-k(pH - pH_0)}} + R_0 \quad (5) $$

Experimental data fitting yielded parameters L = 1.5, k = 2.0, pH0 = 7.8, and R0 = 0.23, where R0 is the baseline offset representing the minimum ratio value, L is the amplitude controlling the total vertical span of the transition, pH0 is the midpoint pH where the transition is steepest, and k is the steepness parameter determining the sharpness of the transition.

## 3. Results and discussion

### 3.1. Oxygen imaging on a planar optode

This section presents several experiments in order to demonstrate the capacity to obtain spatiotemporal oxygen imaging using the oxygen configuration set-up (Fig. 1a). Oxygen is one of the most important analytes sensed by optical methods. State-of-the-art optical oxygen sensors rely on phosphorescent indicators; their luminescence is quenched dynamically by molecular oxygen: i.e. both intensity and the decay time decrease to a similar extent. Oxygen sensing materials for imaging applications are available in two main formats: planar optodes (a thin, flexible sheet or membrane coated chemical indicators, also referred to as planar foils) [20] and nano- or microparticles [33,44,45]. Both most commonly rely on indicator dyes embedded into polymers, although other forms of nanomaterials exists, including dendrimer-protected phosphorescent dyes [46] and dye conjugates with proteins or dextran [47]. For this demonstration we selected the material that relies on platinum(II) tetra(4-fluorophenyl)tetrabenzoporphyrin (PtTPTBPF) embedded in polystyrene [48] (Fig. 3a depicts the chemical structure of the indicator). This indicator combines efficient absorption in the red part of the electromagnetic spectrum, emission in NIR region, good photostability and is commercially available. Other popular complex such as platinum(II) meso-tetra(pentafluorophenyl)porphine (PtTFPP, CAS: 109781-47-7) or analogous palladium(II) derivatives, that are commonly utilized to prepare sensors for measuring trace oxygen (CAS: 1092095-11-8 and 72076-09-6) can be read-out in exactly the same manner.

The lifetime response to oxygen accessed via RLD showed good correlation with reference time-domain measurements, validating the
<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Oxygen mapping on a planar optode: (a) chemical structure of PtTPTBPF indicator and cross-section of the planar optode. (b) Calibration curve showing relationship between lifetime ($\tau$) and $pO_2$ obtained in the time-domain measurement and with the RLD imaging method (with 2 different window durations, $\Delta t$ = 15 µs and $\Delta t$ = 25 µs) with their respective Stern–Volmer fit (c) Time-resolved oxygen mapping (0.1–0.6 s) with fast responding foil in a flow-through cell (3.5 cm in diameter). The Supplementary Video 1 corresponding to this experiment is available in the Supporting Information. (d) Temporal evolution of spatially averaged $pO_2$ within the cell. (e) Oxygen gradient in a cuvette simulating sediment processes experiment. Anoxic conditions on the bottom of the cuvette are achieved by adding 5% wt. aqueous solution of sodium sulfite. (f) Sensor foil response to different oxygen concentrations obtained with a jet of nitrogen in surrounding air (see corresponding Supplementary Video 2).</figcaption></figure>

optical performance of the system (Fig. 3b). The response exhibits non-linear behavior, which is characteristic of most oxygen sensors and well-documented in the literature [40]. This non-linearity results from the indicator dye being distributed between two distinct polymer microenvironments with different oxygen permeabilities. In fact, the luminescence decays (Figure S2 Supporting Information) exhibit a mono-exponential behavior at low oxygen concentrations and becomes slightly non-monoexponential at larger concentrations. For modeling this non-linear response, the modified Stern–Volmer equation (Eq. (3)) was employed. Fitting our experimental RLD data yields parameters $f = 0.83$, $K_{SV1} = 0.23 \text{ kPa}^{-1}$, and $\tau_0 = 52.6 \text{ µs}$. In comparison, fitting the lifetimes extracted from decay data gives $f = 0.80$, $K_{SV1} = 0.32 \text{ kPa}^{-1}$, and $\tau_0 = 57 \text{ µs}$. A slight deviation of $\tau_0$ is observed at $pO_2 = 0 \text{ kPa}$ that could be explained by inaccuracy in obtaining true $pO_2 = 0 \text{ kPa}$ in the flow-through cell. Nevertheless, the overall agreement between

both measurement methods is remarkably close, validating the RLD approach for practical lifetime measurements.

From images recorded at different uniform oxygen concentration during calibration, we assessed that the precision is ± 0.5 kPa (1σ), at a spatial resolution of 200 µm at 12 frames per second (fps). In Fig. 3c, we alternated the gas feed between air ($pO_2$ = 20.95 kPa) and pure nitrogen ($pO_2$ = 0 kPa) during measurements. The test flow-through cell consisted of a small isolated cylinder (3.5 cm in diameter) with inlet and outlet pipes, featuring a glass face to enable visual observation. Fig. 3c,d demonstrates temporal response capabilities at 12 fps, allowing real-time visualization of oxygen dynamics. The current response limitations stem primarily from the speed of sample oxygenation and deoxygenation rather than the intrinsic capabilities of our optical system. This demonstration also effectively simulates the behavior of pressure-sensitive paints (PSPs).

To demonstrate spatial mapping capabilities, we simulated oxygen distribution patterns characteristic of marine sediments and rhizosphere environments (Fig. 3e). This was achieved by first placing the foil in a cuvette containing oxygenated distilled water, followed by introducing a 5% wt. aqueous sodium sulfite solution to the bottom of the cuvette to create localized anoxic conditions. Subsequently, we evaluated the sensor's dynamic response by exposing it to a targeted nitrogen jet using a glass tube with a 0.7 cm diameter (Fig. 3f, and Supplementary Video 2). These experiments effectively demonstrate the system's dual capability to precisely resolve spatial oxygen gradients at full camera resolution and 12 fps while simultaneously capturing rapid and fully reversible temporal fluctuations in oxygen concentration.

Although for this demonstration we exclusively used the planar oxygen sensors, water-dispersible nanoparticles that rely on the same indicator dye also can be effectively used, for example for oxygen mapping in biological environments, as recently demonstrated using a (now-discontinued) Sensicam camera [49]. These nanoparticles have versatile applications: they can be integrated into microfluidic chips, injected into corals [49], or even utilized for intracellular $O_2$ measurements [50].

## 3.2. Temperature imaging in fluid flows using nanoparticle dispersion

We demonstrate temperature imaging within a laser sheet in flows using the temperature configuration set-up (Fig. 1b). Fluorescent dyes, often rhodamines, are used to image temperature in liquids. For ratio-metric measurements, two dyes with different temperature sensitivities are dissolved in the fluid [16,51] but such measurements can suffer from optical-depth dependent spectral distortion, in particular by self-absorption [51], and auto-fluorescence. For state-of-the-art temperature imaging, especially in-vivo, lifetime imaging, for example of lanthanide-doped nanoparticles with decay time of several hundred microseconds, is proposed [52,53]. As a temperature probe, we selected zirconium(IV) pyridinedipyrrrolide complex Zr(MesIPDPt-BuPh)₂ [5], Fig. 4a, incorporated into water dispersible nanoparticles composed of gas-blocking poly(vinylidene chloride-co-acrylonitrile) core with polar groups from methacrylic acid on the surface. The complex exhibits thermally-activated delayed fluorescence (TADF) with strong temperature-dependent emission lifetimes ($d\tau/dT \sim -2.7\%/K$ at 25 °C). With lifetimes around 100 µs at room temperature, this material is ideally suited for our imaging platform.

Fig. 4b demonstrates that the RLD imaging system successfully captures the lifetime dependency of the TADF following an Arrhenius-type model [41] (see Eq. (4)), though with some underestimation from reference time-domain measurements. Fitting our experimental RLD data yields parameters $k_0 = 0.0016$, $k_1 = 57.1$, and $\Delta E_{S1-T1} = 0.23$ eV. In comparison, fitting the lifetimes extracted from decay (Figure S3, Supporting Information) data gives $k_0 = -0.0015$, $k_1 = 21.8$, and $\Delta E_{S1-T1} = 0.20$ eV. This difference arises from the non-monoexponential behavior observed at the initial phase of the decay
<figure><img src="image_7.png" /><figcaption>**Fig. 4.** Laser-induced luminescence for temperature imaging in water: (a) chemical structures of Zr(MesIPDPt-BuPh)₂ indicator and PVA-MAA matrix. (b) Calibration curve showing relationship between TADF lifetime and temperature obtained in time-domain reference measurements and with our RLD imaging system (fit with an Arrhenius type model). (c) Experimental setup for colder water injection into warmer water with labeled components. (d-f) Time series of temperature images at 0.0 s, 0.15 s, and 0.3 s during the injection (7 Hz acquisition, 1 pixel = 500 µm). Inset histogram shows temperature distribution in the uniform region marked in (f). Color scale indicates temperature in °C (see the corresponding Supplementary Video 3).</figcaption></figure>

curves (Figure S3 in Supporting Information), which increases the light captured in window 1 and consequently reduces the calculated ratio.

To evaluate dynamic temperature mapping capabilities, we performed a syringe injection experiment (Fig. 4c) inspired by Abram et al. (2020) [25], where colder fluid (30 °C) was injected into a warmer fluid (59 °C) in a cuvette (Fig. 4c). Both fluids were aqueous dispersions of the nanoparticles (0.5 mg/mL). A laser was used to form a thin laser sheet to excite only a thin volume of fluid and provide a 2-D cut of the temperature field. The system successfully captured evolving temperature fields at 7 Hz (Fig. 4d-f), resolving mixing patterns and thermal gradients with high spatial and temporal precision. The effective measurement precision during this dynamic experiment was approximately ±1 °C, primarily limited by the dilution required for larger volume. Increasing concentration of the nanoparticles in the dispersion, increasing dye content in the nanoparticles or designing an even brighter probe with similar temperature response would enhance the brightness and is therefore expected to further improve the precision or enable higher frame rate at the same precision. Nevertheless, already now our system offers comparable performance to Abram et al. (2020) [25] at significantly lower cost and complexity.

One key advantage here is that the luminescence tracer being water dispersible nanoparticles is unlike micron-sized phosphor particles, not subject to sedimentation, and can be used for experiments over long time scales.

The technique would be applicable in both liquid and slow gas flows using additional micron-size particles for simultaneous velocimetry measurements. Its single-camera set-up is simpler than color-based methods that require image registration, angle-dependent corrections, or position-dependent calibration like thermographic liquid crystals (TLC) imaging. Although here we demonstrate exclusively the application of temperature-sensitive nanoparticles, the method is fully transferable to oxygen-sensitive luminescent particles as, for instance, used by Murniati and coauthors [33,34].

### 3.3. pH imaging via DLR

The third example demonstrates suitability of the new system for pH mapping using the pH configuration set-up (Fig. 1a). Optical measurement of pH is commonly realized with the help of absorptiometric or luminescent indicators [6]. Since luminescence intensity measurement is very ambiguous and from a multitude of luminescent indicators only a few rather exotic probes are suitable for self-referenced decay time read-out, other referencing schemes are commonly utilized. Accordingly, pH measurement with fluorescent indicators is commonly done in a ratiometric 2-wavelength mode or with help of DLR method [35, 36] that can be employed both in frequency and time domain. DLR sensing and imaging requires addition of a luminescent reference with pH-independent emission, similar spectral properties to those of the indicator but significantly different luminescence decay time (typically in microsecond or millisecond time domain). Importantly, the same scheme can be applied for sensing and imaging of any other quantity that relies on a fluorescent indicator, such as chloride [54], potassium [55], sodium [56], ammonia [57] or carbon dioxide [58]. Most of the older reports utilize the reference materials that rely on ruthenium(II) complexes embedded into gas-blocking nano- and microparticles and, due to the relatively short decay time of those dyes ($\tau < 6$ µs) are not compatible with this new imaging system. However, a great number of inorganic phosphors with significantly longer decay times are available in form of microcrystalline powders and can be utilized as referenced materials.

To demonstrate DLR mode imaging capabilities, we employed a previously reported dual-component system combining an aza-BODIPY pH indicator [42] (Fig. 5a) with Egyptian blue microcrystalline powder [43] as a pH-independent reference material with a lifetime of approximately 120 µs. Both components can be excited with a red LED and emit in the far-red/NIR part of the spectrum (Fig. 5b). While the pH indicator is fluorescent and exhibits “on-off” behavior, i.e. strong emission in the protonated form and virtually no emission in the deprotonated form (see Fig. 5b), the reference material shows constant emission. Fig. 5c demonstrates that utilization of the 2-window scheme ensures reproducible response of the Ratio to pH values, showing a typical sigmoidal curve shape (Eq. (5) in Methods section). The dynamic range of this sensing material is from pH 5 to pH 9.5, but the scheme is adaptable to any other fluorescent indicator. Due to the sigmoidal curve shape, estimated precision on uniform pH samples varies significantly from a minimum at $\delta_{pH} = 0.06$ (1σ) at the maximum of sensitivity around pH 7.5 to $\delta_{pH} = 0.6$ (1σ) at the range extremes (pH 5 and pH 9.5). This estimation is made at a spatial resolution of 200 µm at 12 frames per second (fps). The simulation experiment in a cuvette (see Fig. 5d and Supplementary Video 4) demonstrates that pH gradients can be clearly resolved, and complex dynamics tracked. Table 1 summarizes the analytical performance achieved across the three demonstrated sensing applications.
<figure><img src="image_8.png" /><figcaption>**Fig. 5.** pH imaging on a planar optode using dual lifetime referencing: (a) chemical structure of the aza-BODIPY pH-indicator and the cross-section of the planar sensor material. (b) Normalized emission spectra of the material at pH 4 and pH 10 (excitation at 625 nm), and transmission of the RG 9 long-pass filter. (c) Calibration curve showing ratio response (R) as a function of pH using the DLR method for three replicates. (d) pH imaging with help of the sensor foil initially in buffer (pH 7.5), with introduction of acid (1 M HCl, left) and base (1 M NaOH, right), revealing pH gradients between pH 6–9 (see Supplementary Video 4).</figcaption></figure>

<table><caption>**Table 1**
Performance results of the three sensing applications.</caption><thead><tr><th>Application</th><th>Oxygen</th><th>Temperature</th><th>pH</th></tr></thead><tbody><tr><td>Luminophore</td><td>PtTPTBPF</td><td>Zr(MesIPDPt-BuPh)<sub>2</sub></td><td>aza-BODIPY +<br>Egyptian blue<br>(reference)</td></tr><tr><td>Matrix</td><td>polystyrene</td><td>PViCl-PAN</td><td>HydroMed D4</td></tr><tr><td>Sensor Format</td><td>planar optode</td><td>nanoparticles</td><td>planar optode</td></tr><tr><td>Method</td><td>RLD</td><td>RLD</td><td>DLR</td></tr><tr><td>Lifetime</td><td>~ 20 to 50 μs</td><td>~ 50 to 150 μs</td><td>~ 125 μs</td></tr><tr><td>Precision (1σ)</td><td>±0.5 kPa</td><td>±1 °C</td><td>δ<sub>pH</sub> = 0.06</td></tr><tr><td>Spatial<br>resolution</td><td>200 μm</td><td>500 μm</td><td>300 μm</td></tr><tr><td>Sampling rate</td><td>12 Hz</td><td>7 Hz</td><td>4 Hz</td></tr></tbody></table>

### 3.4. Comparative performance and technical constraints

The selection of appropriate instrumentation represents a critical decision point for luminescence lifetime imaging implementations. While multi-exposure accumulation techniques are established, widespread adoption has been constrained by the cost of specialized equipment and limited camera availability. To quantify the advantages of our approach, we compare the Ximea camera employed in this study against both commercially available and discontinued systems previously used for decay-time-based sensing applications.

Table 2 presents a comprehensive comparison of four camera systems spanning different technological generations. The selected systems represent: (i) discontinued interline CCD cameras still used in many laboratories (Sensicam, PCO 1600), (ii) current state-of-the-art commercial FLIM system (PCO FLIM X), and (iii) our consumer CMOS approach (Ximea).

Our analysis reveals several key performance advantages of the Ximea platform. Most notably, the camera achieves lowest read-out

noise performance (2.4 e⁻) and highest quantum efficiency (66%), while offering the highest spatial resolution (2464 × 2056 pixels) among all compared systems. The cost reduction is dramatic—at €1,500, the Ximea represents a 20-fold decrease compared to current commercial FLIM systems, yet delivers superior performance for microsecond decay probes. The Ximea's lower full well capacity (9.8k e⁻) compared to other systems initially appears limiting. However, the superior read-out noise performance maintains effective dynamic range (4083), comparable to the other systems.

The system's operational boundaries are defined by several technical constraints. The minimum measurable lifetime is limited by the electronic shutter switching speed (1 µs) and minimum exposure duration (15 µs), restricting applications to decay times exceeding 10 µs. The maximum modulation frequency, which is the inverse of the minimum modulation period, is determined by the addition of minimum exposure time and minimum inter-exposure delay, yielding a practical limit of 10 kHz. These constraints accommodate most phosphorescent and TADF materials but exclude measurements of shorter lifetimes, which require specialized systems such as the PCO FLIM combined with appropriate laser sources capable of measuring lifetimes ranging from 100 ps to 100 µs [26].

Maximum sampling rate performance demonstrates significant improvements of our proposed system over discontinued systems, with theoretical capability of 37 fps versus 6–15 fps for Sensicam or PCO 1600 as used, for instance, in the study of Murniati et al. (2016, 2025) [33,34]. In practice, our demonstrated rates (up to 12 fps, Table 1) are determined by signal-to-noise requirements rather than camera limitations. The sampling rate scales inversely with the number of accumulated exposures needed for adequate SNR, following: pixel count ∝ $N_{tot} \times t_{on}$ until saturation.

Modifications in the optode chemistry that result in increased brightness would allow for significant improvement of the frame rate. Although increasing thickness of the sensing layer may appear straightforward, it would result in a significant increase of the sensor response time in case of diffusion-based oxygen and pH sensors so that the signal captured at high fps may not anymore reflect the real analyte concentration in the analyzed media. Therefore, a more elaborated solution may include creation of structures in the sensor material that facilitate the analyte diffusion, for example as realized in some pressure-sensitive paint with help of nanoparticles [59]. The frame rate can also be boosted by utilization of brighter indicators giving more luminescence signal for the layers of the same thickness. Although molar absorption coefficients of the oxygen and pH indicators utilized in this work are rather high, the luminescence quantum yields are moderate in both cases (around 20%) [42,60]. For instance, cyclometalated iridium coumarin complexes that feature particularly high brightness [61] may represent an interesting alternative for faster measurements of oxygen dynamics. However, their moderate photostability presents a limitation that could be overcome through chemical modification or by restricting applications to short-term measurements.

Beyond modifying the sensing chemistry to increase brightness, system performance for applications requiring higher temporal or spatial resolution can be enhanced through improved illumination conditions or upgraded camera hardware. Indeed, more recent CMOS sensors should also offer multi-exposure-in-one-frame capability, although this functionality remains, to the best of our knowledge, unimplemented by camera manufacturers and should become available as the technology matures. Unlike CCDs, CMOS sensors continue to be developed and it is likely that more sensitive sensors supporting this mode will be available in the future.

The system's low read-out noise and high frame rate were instrumental in applying the accumulation strategy to dynamic fluid thermometry for the first time, as demonstrated in Section 3.2. The accumulation strategy can achieve high SNR with much lower peak power sources than the single shot lifetime approach using more expansive pulsed lasers (see e.g. [25]).

In summary, the data demonstrates that, for lifetimes higher than 10 µs, our system delivers comparable or superior performance characteristics across key metrics at significantly lower costs.
<table><caption>**Table 2**
Comparison of camera systems for decay-time-based sensing applications.</caption><thead><tr><th>Camera</th><th>Sensicam sensimod</th><th>PCO 1600</th><th>PCO FLIM X</th><th>Ximea MX050CG-SY-UB</th></tr></thead><tbody><tr><td>Status</td><td>discontinued</td><td>discontinued</td><td>available</td><td>available (used in this study)</td></tr><tr><td>Technology</td><td>Interline CCD</td><td>Interline CCD</td><td>Double tap CMOS</td><td>CMOS</td></tr><tr><td>Max. Sampling rate (Hz)</td><td>6</td><td>15</td><td>45</td><td>37</td></tr><tr><td>Read-out noise (e<sup>-</sup>)</td><td>8</td><td>10</td><td>15</td><td>2.4</td></tr><tr><td>Full well capacity (e<sup>-</sup>)</td><td>25k</td><td>40k</td><td>53k</td><td>9.8k</td></tr><tr><td>Dynamic range*</td><td>3125</td><td>4000</td><td>3533</td><td>4083</td></tr><tr><td>Resolution (pixels)</td><td>1280 ×1024</td><td>2048 ×2048</td><td>1008 ×1008</td><td>2464 ×2056</td></tr><tr><td>Pixel size (µm)</td><td>6.7</td><td>7.4</td><td>5.6</td><td>3.5</td></tr><tr><td>Quantum efficiency (%)</td><td>42</td><td>55</td><td>40</td><td>66</td></tr><tr><td>Min. exposure time (µs)</td><td>0.250</td><td>0.5</td><td>-</td><td>15</td></tr><tr><td>Max. mod. freq. (kHz)</td><td>0.350</td><td>50</td><td>40 000</td><td>10</td></tr><tr><td>Price (€) (Year)</td><td>~10k (~ 2010)</td><td>~15k (~2010)</td><td>~35k (2025)</td><td>~1.5k (2025)</td></tr></tbody></table>

## 4. Conclusions

This work presents a resource-efficient lifetime imaging platform that achieves an order-of-magnitude reduction in system costs (€2,000–4,000 vs €30,000+) while maintaining analytical performance across oxygen, temperature, and pH sensing applications. The platform demonstrates that quantitative lifetime-based measurements can be achieved using a CMOS-based accumulation technique with only essential components: a camera, optical filter, microcontroller, and light source. Due to CMOS sensor limitations, the platform’s operational include minimum measurable lifetimes of 10 µs and maximum modulation frequencies of 10 kHz, excluding shorter-lived fluorescence applications which require specialized high-frequency instrumentation. Within these constraints, the system delivers robust analytical performance across diverse sensing scenarios. The spatial and temporal precision metrics across all sensing modalities (Table 1) validate the platform’s quantitative analytical capabilities.

Notably, the TADF-based temperature imaging, achieving ±1 °C precision at 7 Hz, represents the first demonstration of lifetime-based thermometry with TADF materials in macroscopic dynamic fluid systems, marking a significant advancement in flow diagnostics. The RLD read-out scheme is compatible with probes exhibiting long-lived emission (microseconds to milliseconds, as in phosphorescence or TADF) and enables critical applications such as oxygen and temperature mapping. Both planar sensors and water-dispersible nanoparticles can effectively track rapid distribution changes. A significant advantage of RLD mode is the elimination of background fluorescence, allowing these materials to function in various environments — including photosynthetically active systems — without modifications.

The open-source Clicc software available at https://gitlab.com/groussea/clicc ensures broad accessibility and reproducibility, facilitating rapid adoption in resource-constrained research environments. By providing a resource-efficient and open-source lifetime imaging solution, this method democratizes access to advanced imaging capabilities across disciplines ranging from chemistry to biomedical sciences, aerospace, and geosciences.

Future developments could include optimizing gating schemes for both DLR and RLD methods by adjusting exposure window duration and delay parameters, testing alternative DLR schemes (one window during excitation and two windows during emission) for multi-parameter imaging, and implementing multi-analyte detection using RGB camera capable of multi-exposure acquisition within single frames. Performance can be further enhanced through improved illumination or higher-specification sensors.

## CRediT authorship contribution statement

**Gauthier Rousseau:** Writing – review & editing, Writing – original draft, Supervision, Software, Methodology, Investigation, Formal analysis, Data curation.
**Marianne Pons:** Writing – review & editing, Software, Investigation.
**Hessel Adelerhof:** Writing – review & editing, Software, Investigation.
**Nathan Pellerin:** Writing – review & editing, Software, Investigation.
**Mart Giesbergen:** Writing – review & editing, Software, Investigation.
**Bastien Carde:** Writing – review & editing, Software, Investigation.
**Martin Wolff:** Writing – review & editing, Software, Investigation.
**Koen Blanckaert:** Writing – review & editing, Supervision, Funding acquisition, Conceptualization.
**Sergey M. Borisov:** Writing – review & editing, Writing – original draft, Supervision, Resources, Methodology, Investigation, Conceptualization.
**Benoit Fond:** Writing – review & editing, Writing – original draft, Supervision, Methodology, Investigation, Conceptualization.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgments

This work was partly supported by Austrian Federal Ministry of Woman, Science and Research and French Ministry of Higher Education and Research and Ministry for Europe and Foreign Affairs; Project “Luminescent materials for optical measurements of pressure and temperature in aerospace research” (Project number: MULT 12/2023). We are grateful to the TU Wien workshop team for their assistance in designing the experimental set-ups, to Matthias Schwar (TU Graz) for his help with foil and buffer preparation. We thank Sacha Hirsch (Onera) and Maria Klepikova, Arwa Rashed, Yves Méheust, and Joris Heyman (Geosciences Rennes) for their helpful insights during this study.

## Appendix A. Supplementary data

Supplementary material related to this article can be found online at https://doi.org/10.1016/j.snb.2025.138849.

<footnote>*Dynamic range = Full well capacity/Read-out noise</footnote>
## Data availability

The experimental data generated during this study are available on Zenodo at https://doi.org/10.5281/zenodo.15662176. The open-source Clicc (Cumulative Lifetime Imaging with CMOS-Camera) hardware control code implemented in Python and Arduino is available at https://gitlab.com/groussea/clicc. Supplementary Videos 1, 2, 3, and 4 demonstrating the real-time imaging capabilities can be viewed and downloaded at https://tucloud.tuwien.ac.at/index.php/s/kWNqpmCoppYDyl. Descriptions of the Supplementary Videos are provided in the Supplementary Information.

## References

[1] M. Schäferling, The art of fluorescence imaging with chemical sensors, Angew. Chem. Int. Ed. 51 (15) (2012) 3532-3554, http://dx.doi.org/10.1002/anie.201105459.

[2] K. Koren, S.E. Zieger, Optode based chemical imaging—possibilities, challenges, and new avenues in multidimensional optical sensing, ACS Sensors 6 (5) (2021) 1671–1680, http://dx.doi.org/10.1021/acsensors.1c00480.

[3] R.I. Dmitriev, D.B. Papkovsky, Intracellular probes for imaging oxygen concentration: How good are they? Methods Appl. Fluoresc. 3 (3) (2015) 034001, http://dx.doi.org/10.1088/2050-6120/3/3/034001.

[4] O. Mizukami, A. Katano, S. Shiozaki, T. Yoshihara, N. Goda, S. Tobita, In vivo o2 imaging in hepatic tissues by phosphorescence lifetime imaging microscopy using ir(iii) complexes as intracellular probes, Sci. Rep. 10 (1) (2020) 21053, http://dx.doi.org/10.1038/s41598-020-76878-6.

[5] A. Russegger, A.C. Debruyne, D.C. Berrio, S. Fuchs, J. Marzi, K. Schenke-Layland, R.I. Dmitriev, S.M. Borisov, Bright and photostable tADF-emitting zirconium(iv) pyridinedipyrrrolide complexes: Efficient dyes for decay time-based temperature sensing and imaging, Adv. Opt. Mater. 11 (9) (2023) 2202720, http://dx.doi.org/10.1002/adom.202202720.

[6] A. Steinegger, O.S. Wolfbeis, S.M. Borisov, Optical sensing and imaging of ph values: Spectroscopies, materials, and applications, Chem. Rev. 120 (22) (2020) 12357–12489, http://dx.doi.org/10.1021/acs.chemrev.0c00451.

[7] M. Kamiya, K. Johnsson, Localizable and highly sensitive calcium indicator based on a bodipy fluorophore, Anal. Chem. 82 (15) (2010) 6472–6479.

[8] E.C. Ximendes, U. Rocha, B. del Rosal, A. Vaquero, F. Sanz-Rodríguez, L. Monge, F. Ren, F. Vetrone, D. Ma, J. García-Solé, C. Jacinto, D. Jaque, N. Fernández, In vivo ischemia detection by luminescent nanothermometers, Adv. Heal. Mater. 6 (4) (2017) 1601195, http://dx.doi.org/10.1002/adhm.201601195.

[9] K. Koren, K.E. Brodersen, S.L. Jakobsen, M. Kühl, Optical sensor nanoparticles in artificial sediments—a new tool to visualize o2 dynamics around the rhizome and roots of seagrasses, Environ. Sci. Technol. 49 (4) (2015) 2286–2292, http://dx.doi.org/10.1021/es505734b.

[10] J. Santner, M. Larsen, A. Kreuzeder, R.N. Glud, Two decades of chemical imaging of solutes in sediments and soils – a review, Anal. Chim. Acta 878 (2015) 9–42, http://dx.doi.org/10.1016/j.aca.2015.02.006.

[11] M.H. Kaufman, M.B. Cardenas, J. Buttles, A.J. Kessler, P.L. Cook, Hyporheic hot moments: Dissolved oxygen dynamics in the hyporheic zone in response to surface flow perturbations, Water Resour. Res. 53 (8) (2017) 6642–6662, http://dx.doi.org/10.1002/2016WR02096.

[12] M. Staal, S.M. Borisov, L.F. Rickelt, I. Klimant, M. Kühl, Ultrabright planar optodes for luminescence life-time based microscopic imaging of o2 dynamics in biofilms, J. Microbiol. Meth. 85 (1) (2011) 67–74, http://dx.doi.org/10.1016/j.mimet.2011.01.021.

[13] A. Blancke Soares, R. Meier, G. Liebsch, S. Schwenk-Zieger, M. Kirmaier, S. Theurich, M. Widmann, M. Canis, O. Gires, F. Haubner, High-resolution spatiotemporal phe and po2 imaging in head and neck and oesophageal carcinoma cells, Cancer Metab. 9 (1) (2021) 21, http://dx.doi.org/10.1186/s40170-021-00257-6.

[14] T. Liu, J.P. Sullivan, K. Asai, C. Klein, Y. Egami, Pressure and Temperature Sensitive Paints, Springer Cham, 2021, http://dx.doi.org/10.1007/978-3-030-68056-5.

[15] D. Peng, Y. Liu, Fast pressure-sensitive paint for understanding complex flows: from regular to harsh environments, Exp. Fluids 61 (1) (2019) 8, http://dx.doi.org/10.1007/s00348-019-2839-6.

[16] K. Hishida, J. Sakakibara, Combined planar laser-induced fluorescence–particle image velocimetry technique for velocity and temperature fields, Exp. Fluids 29 (Suppl. 1) (2000) S129–S140, http://dx.doi.org/10.1007/s003480070015.

[17] M. Raffel, C.E. Willert, F. Scarano, C.J. Kähler, S.T. Wereley, J. Kompenhans, Particle Image Velocimetry: A Practical Guide, Springer, Cham, 2018, http://dx.doi.org/10.1007/978-3-319-68852-7.

[18] S. Ahmerkamp, F.M. Jalaluddin, Y. Cui, D.R. Brumley, C.O. Pacherres, J.S. Berg, R. Stocker, M.M. Kuypers, K. Koren, L. Behrendt, Simultaneous visualization of flow fields and oxygen concentrations to unravel transport and metabolic processes in biological systems, Cell Rep. Methods 2 (5) (2022) 100216, http://dx.doi.org/10.1016/j.crmeth.2022.100216.

[19] C. Abram, B. Fond, A. Heyes, F. Beyrau, High-speed planar thermometry and velocimetry using thermographic phosphor particles, Appl. Phys. B-Lasers O 111 (2013) 155–160.

[20] X.-d. Wang, O.S. Wolfbeis, Optical methods for sensing and imaging oxygen: Materials, spectroscopies and applications, Chem. Soc. Rev. 43 (10) (2014) 3666–3761, http://dx.doi.org/10.1039/C4CS00039K.

[21] X.-d. Wang, O.S. Wolfbeis, R.J. Meier, Luminescent probes and sensors for temperature, Chem. Soc. Rev. 42 (2013) 7834–7869, http://dx.doi.org/10.1039/C3CS60102A.

[22] S. Yingli, L. José, F. Nuria, J. Daniel, X. Erving, In vivo spectral distortions of infrared luminescent nanothermometers compromise their reliability, ACS Nano 14 (4) (2020) 4122–4133, http://dx.doi.org/10.1021/acsnano.9b08824.

[23] G. Holst, O. Kohls, I. Klimant, B. König, M. Kühl, T. Richter, A modular luminescence lifetime imaging system for mapping oxygen distribution in biological samples, Sensors Actuators B: Chem. 51 (1) (1998) 163–170, http://dx.doi.org/10.1016/S0925-4005(98)00232-9.

[24] D. Peng, Y. Liu, X. Zhao, K.C. Kim, Comparison of lifetime-based methods for 2d phosphor thermometry in high-temperature environment, Meas. Sci. Technol. 27 (9) (2016) 095201, http://dx.doi.org/10.1088/0957-0233/27/9/095201.

[25] C. Abram, I. Wilson Panjikkaran, S. Ogugua, B. Fond, ScVO₄:Bi³⁺ thermochromic phosphor particles for fluid temperature imaging with sub °C precision, Opt. Lett. 45 (14) (2020) 3893–3896, http://dx.doi.org/10.1364/OL.392088.

[26] K. Koren, M. Mößhammer, V.V. Scholz, S.M. Borisov, G. Holst, M. Kühl, Luminescence lifetime imaging of chemical sensors—a comparison between time-domain and frequency-domain based camera systems, Anal. Chem. 91 (5) (2019) 3233–3238, http://dx.doi.org/10.1021/acs.analchem.8b05869.

[27] M. Larsen, S.M. Borisov, B. Grunwald, I. Klimant, R.N. Glud, A simple and inexpensive high resolution color ratiometric planar optode imaging approach: Application to oxygen and ph sensing, Limnol. Ocean.: Methods 9 (9) (2011) 348–360, http://dx.doi.org/10.4319/lom.2011.9.348.

[28] H. Eghartner, H. Wilsche, S.M. Borisov, T. Mayr, Low cost referenced luminescent imaging of oxygen and ph with a 2-ccd colour near infrared camera, Analyst 139 (19) (2014) 4924–4933, http://dx.doi.org/10.1039/C4AN00783B.

[29] S. Ahmerkamp, C.O. Pacherres, M. Mosshammer, M. Godefroid, M. Wind-Hansen, M.M. Kuypers, L. Behrendt, K. Koren, M. Kühl, Novel approach for lifetime-proportional luminescence imaging using frame straddling, ACS Sensors 9 (10) (2024) 5531–5540, http://dx.doi.org/10.1021/acssensors.4c01828.

[30] A. Goncharov, Z. Gorocs, R. Pradhan, B. Ko, A. Ajmal, A. Rodriguez, D. Baum, M. Veszpremi, X. Yang, M. Pindrys, T. Zheng, O. Wang, J.C. Ramella-Roman, M.J. McShane, A. Ozcan, Insertable glucose sensor with a compact and cost-effective phosphorescence lifetime imager and machine learning, ACS Nano 18 (34) (2024) 23365–23379, http://dx.doi.org/10.1021/acsnano.4c06527.

[31] K.K. Sharman, A. Periasamy, H. Ashworth, J.N. Demas, Error analysis of the rapid lifetime determination method for double-exponential decays and new windowing schemes, Anal. Chem. 71 (5) (1999) 947–952, http://dx.doi.org/10.1021/ac981050d.

[32] S.P. Chan, Z.J. Fuller, J.N. Demas, B.A. DeGraff, Optimized gating scheme for rapid lifetime determinations of single-exponential luminescence lifetimes, Anal. Chem. 73 (18) (2001) 4486–4490, http://dx.doi.org/10.1021/ac0102361.

[33] E. Murniati, D. Gross, H. Herlina, K. Hancke, R.N. Glud, A. Lorie, Oxygen imaging at the sediment-water interface using lifetime-based laser induced fluorescence (r lif) of nano-sized particles, Limnol. Ocean.: Methods 14 (8) (2016) 506–517, http://dx.doi.org/10.1002/lim0.310108.

[34] E. Murniati, A. Philippe, O. Eiff, H. Herlina, A combined intensity and lifetime-based laser-induced fluorescence technique (i-r lif) with dye-doped nanobeads tracer for dissolved oxygen imaging below the water surface, Exp. Fluids 66 (7) (2025) 1–19, http://dx.doi.org/10.1007/s00348-025-04062-5.

[35] I. Klimant, C. Huber, G. Liebsch, G. Neurauter, A. Stangelmayer, O.S. Wolfbeis, Dual lifetime referencing (dlr)—a new scheme for converting fluorescence intensity into a frequency-domain or time-domain information, in: New Trends in Fluorescence Spectroscopy: Applications To Chemical and Life Sciences, Springer, 2001, pp. 257–274.

[36] G. Liebsch, I. Klimant, C. Krause, O.S. Wolfbeis, Fluorescent imaging of ph with optical sensors using time domain dual lifetime referencing, Anal. Chem. 73 (17) (2001) 4354–4363, http://dx.doi.org/10.1021/ac0100852.

[37] S.M. Borisov, G. Nuss, W. Haas, R. Saf, M. Schmuck, I. Klimant, New nir-emitting complexes of platinum(ii) and palladium(ii) with fluorinated benzoporopyrins, J. Photochem. Photobiol. A: Chem. 201 (2–3) (2009) 128–135, http://dx.doi.org/10.1016/j.jphotochem.2008.10.003.

[38] L.H. Hutter, B.J. Müller, K. Koren, S.M. Borisov, I. Klimant, Robust optical oxygen sensors based on polymer-bound nir-emitting platinum(ii)–benzoporopyrins, J. Mater. Chem. C 2 (36) (2014) 7589–7598, http://dx.doi.org/10.1039/C4TC00983E.

[39] A.C. Debruyne, I.A. Okkelman, N. Heymans, C. Pinheiro, A. Hendrix, M. Nobis, S.M. Borisov, R.I. Dmitriev, Live microscopy of multicellular spheroids with the multimodal near-infrared nanoparticles reveals differences in oxygenation gradients, ACS Nano 18 (19) (2024) 12168–12186.

[40] E.R. Carraway, J.N. Demas, B.A. DeGraff, J.R. Bacon, Photophysics and photochemistry of oxygen sensors based on luminescent transition-metal complexes, Anal. Chem. 63 (4) (1991) 337–342, http://dx.doi.org/10.1021/ac00004a007.
[41] G. Liebsch, I. Klimant, O.S. Wolfbeis, Luminescence lifetime temperature sensing based on sol-gels and poly (acrylonitrile) s dyed with ruthenium metal-ligand complexes, Adv. Mater. 11 (15) (1999) 1296-1299, http://dx.doi.org/10.1002/(SICI)1521-4095(199910)11:15%3C1296::AID-ADMA1296%3E3.0.CO;2-B

[42] M. Strobl, T. Rappitsch, S.M. Borisov, T. Mayr, I. Klimant, Nir-emitting azabodipytes-new building blocks for broad-range optical ph sensors, Analyst 140 (21) (2015) 7150-7153, http://dx.doi.org/10.1039/C5AN01389E.

[43] S.M. Borisov, C. Würth, U. Resch-Genger, I. Klimant, New life of ancient pigments: Application in high-performance optical sensing materials, Anal. Chem. 85 (19) (2013) 9371-9377, http://dx.doi.org/10.1021/ac402275g.

[44] M. Moßhammer, K.E. Brodersen, M. Kühl, K. Koren, Nanoparticle- and microparticle-based luminescence imaging of chemical species and temperature in aquatic systems: A review, Microchim. Acta 186 (2) (2019) 126, http://dx.doi.org/10.1007/s00604-018-3202-y.

[45] E. Trampe, K. Koren, A.R. Akkineni, C. Senwitz, F. Krujatz, A. Lode, M. Gelinsky, M. Kühl, Functionalized bioink with optical sensor nanoparticles for o2 imaging in 3d-bioprinted constructs, Adv. Funct. Mater. 28 (45) (2018) 1804411, http://dx.doi.org/10.1002/adfm.201804411.

[46] T.V. Espipova, A. Karagodov, J. Miller, D.F. Wilson, T.M. Busch, S.A. Vinogradov, Two new protected oxyphors for biological oximetry: Properties and application in tumor imaging, Anal. Chem. 83 (22) (2011) 8756-8765, http://dx.doi.org/10.1021/ac2022234.

[47] C. O'Donovan, J. Hynes, D. Yashunski, D.B. Papkovsky, Phosphorescent oxygen-sensitive materials for biological applications, J. Mater. Chem. 15 (27-28) (2005) 2946-2951, http://dx.doi.org/10.1039/B501748C.

[48] S.M. Borisov, G. Nuss, I. Klimant, Red light-excitable oxygen sensing materials based on platinum(ii) and palladium(ii) benzoporphyrins, Anal. Chem. 80 (24) (2008) 9435-9442, http://dx.doi.org/10.1021/ac801521v.

[49] M. Kühl, D.A. Nielsen, S.M. Borisov, In vivo lifetime imaging of the internal o2 dynamics in corals with near-infrared-emitting sensor nanoparticles, ACS Sensors 9 (9) (2024) 4671-4679, http://dx.doi.org/10.1021/acssensors.4c01029.

[50] A.V. Kondrashina, R.I. Dmitriev, S.M. Borisov, I. Klimant, I. O'Brien, Y.M. Nolan, A.V. Zhdanov, D.B. Papkovsky, A phosphorescent nanoparticle-based probe for sensing and imaging of (intra)cellular oxygen in multiple detection modalities, Adv. Funct. Mater. 22 (23) (2012) 4931-4939, http://dx.doi.org/10.1002/adfm.201201387.

[51] W. Chaze, O. Caballina, G. Castanet, F. Lemoine, Spatially and temporally resolved measurements of the temperature inside droplets impinging on a hot solid surface, Exp. Fluids 58 (8) (2017) 96, http://dx.doi.org/10.1007/s00348-017-2375-1.

[52] M. Tan, F. Li, N. Cao, H. Li, X. Wang, C. Zhang, D. Jaque, G. Chen, Accurate in vivo nanothermometry through nir-ii lanthanide luminescence lifetime, Small 16 (48) (2020) 2004118, http://dx.doi.org/10.1002/smll.202004118.

[53] M. Liu, Y. Lai, M. Marquez, F. Vetrone, J. Liang, Short-wave infrared photoluminescence lifetime mapping of rare-earth doped nanoparticles using all-optical streak imaging, Adv. Sci. 11 (11) (2024) 2305284, http://dx.doi.org/10.1002/advs.202305284.

[54] L. Ding, Y. Lian, Z. Lin, Z. Zhang, X.-d. Wang, Long-term quantitatively imaging intracellular chloride concentration using a core-/shell-structured nanosensor and time-domain dual-lifetime referencing method, ACS Sensors 5 (12) (2020) 3971-3978, http://dx.doi.org/10.1021/acssensors.0c01671.

[55] B.J. Müller, S.M. Borisov, I. Klimant, Red- to nir-emitting, bodipy-based, k+-selective fluoroionophores and sensing materials, Adv. Funct. Mater. 26 (42) (2016) 7697-7707, http://dx.doi.org/10.1002/adfm.201603822.

[56] B.J. Müller, T. Rappitsch, C. Staudinger, C. Rüsching, S.M. Borisov, I. Klimant, Sodium-selective fluoroionophore-based optodes for seawater salinity measurement, Anal. Chem. 89 (13) (2017) 7195-7202, http://dx.doi.org/10.1021/acs.analchem.7b01373.

[57] K. Waich, S.M. Borisov, T. Mayr, I. Klimant, Dual lifetime referenced trace ammonia sensors, Sensors Actuators B: Chem. 139 (1) (2009) 132-138, http://dx.doi.org/10.1016/j.snb.2008.10.010.

[58] C. v. Bultzingslowen, A.K. McEvoy, C. McDonagh, B.D. MacCraith, I. Klimant, C. Krause, O.S. Wolfbeis, Sol-gel based optical carbon dioxide sensor employing dual luminophore referencing for application in food packaging technology, Analyst 127 (11) (2002) 1478-1483, http://dx.doi.org/10.1039/b207438g.

[59] M. Kasai, A. Suzuki, Y. Egami, T. Nonomura, K. Asai, A platinum-based fast-response pressure-sensitive paint containing hydrophobic titanium dioxide, Sensors Actuators A: Phys. 350 (2023) 114140, http://dx.doi.org/10.1016/j.sna.2022.114140.

[60] P.W. Zach, S.A. Freunberger, I. Klimant, S.M. Borisov, Electron-deficient near-infrared pt (ii) and pd (ii) benzoporphyrins with dual phosphorescence and unusually efficient thermally activated delayed fluorescence: first demonstration of simultaneous oxygen and temperature sensing with a single emitter, ACS Appl. Mater. Interfaces 9 (43) (2017) 38008-38023, http://dx.doi.org/10.1021/acsami.7b10669.

[61] S.M. Borisov, I. Klimant, Ultrabright oxygen optodes based on cyclometalated iridium (iii) coumarin complexes, Anal. Chem. 79 (19) (2007) 7501-7509, http://dx.doi.org/10.1021/ac0710836.

**Gauthier Rousseau** is a researcher at the Institute of Hydraulic Engineering and Water Resources Management at TU Wien, Austria. He studied physics, earned a Master in Hydrology and Hydrogeology from Sorbonne University and completed his Ph.D. at École Polytechnique Fédérale de Lausanne (EPFL) in 2019 focusing on turbulent flows over rough permeable beds in mountain rivers. His research expertise spans experimental fluid mechanics, mixing processes in porous media, granular flows, and hyporheic zone dynamics. He developed the open-source Python package "opyflow" for optical flow measurements.

**Marianne Pons** has started her Ph.D. at the TU Vienna, Austria in November 2024 at the Institute of Hydraulic Engineering and Water Resources with Prof. Blanckaert. She is working on gravity currents induced by temperature differences and 2D temperature measurements in water. She got her mechanical engineering degree from INSA Lyon, France in 2024.

**Hessel Adelerhof** completed his master's degree in fluids, bio and soft matter at TU Eindhoven in 2024. In spring 2024, he completed a 6-month internship at the Institute of Hydraulic Engineering and Water Resources at TU Wien, working on developing laser-induced lifetime imaging of temperature with an accumulation technique on CMOS Camera.

**Nathan Pellerin** Ph.D. student (2024-2027) at the National Research Institute for Agriculture, Food and Environment (INRAE). He is currently working on the estimation of the hydrological impacts of farm dams over French catchments. His research focuses on assessing the hydrological impacts of farm dams on French catchments. He graduated in 2024 from the University of Grenoble as a hydraulics and hydrology engineer. During his studies, he completed internships in hydrological modeling and in laboratory experiments, on temperature measurement and sediments processes.

**Mart Giesbergen** completed his master's degree in Applied Physics at TU Eindhoven in 2024. He conducted his master's thesis project with the Fluids and Flows group at the University of Technology in Eindhoven. In winter 2024, he completed a 3-month internship at TU Wien at the Institute of Hydraulic Engineering and Water Resources, developing temperature measurements in density current setups.

**Bastien Carde** is a final-year engineering student at INP Grenoble specializing in hydraulics. In 2024, he completed a 3-month internship at TU Wien, where he worked on optimizing cumulative lifetime imaging using CMOS cameras.

**Martin Wolf** is an Electrical Engineering student at TU Wien who works part-time at the Research Unit for Hydraulic Engineering and Environmental Hydromechanics. He applies his expertise in electronics and informatics to develop and implement tools for hydraulic applications.

**Koen Blanckaert** is Full Professor and Head of the Research Unit Hydraulic Engineering and Environmental Hydromechanics at TU Wien, Vienna, Austria, since October 2017. He earned his Ph.D. in Science from EPFL in 2002, followed by postdoctoral positions at EPFL (2002-2004, 2005-2012). He served as Visiting Professor at the Chinese Academy of Sciences, Beijing (2009-2014), Senior Scientist at EPFL (2016), and Associate Professor at Hong Kong University of Science and Technology (2017). His research focuses on geophysical and environmental fluid mechanics, sediment transport, and ecohydraulics. He serves as Associate Editor of the Journal of Hydraulic Engineering (ASCE) and has received the prestigious Schoemaker Award from IAHR.

**Sergey M. Borisov** received his Ph.D. degree in chemistry from the Herzen State Pedagogical University (St. Petersburg, Russia) in 2003. In 2004-2006 he worked in the group of Prof. Otto Wolfbeis at the University of Regensburg (Germany) as a postdoctoral research fellow. He joined the Institute of Analytical Chemistry and Food Chemistry at the Graz University of Technology (Austria) in 2006 where his is currently an associate professor at the same institute. His main research interests are in the chemistry of porphyrins and related compounds, synthesis and application of new luminescent dyes and inorganic phosphors in optical sensing, the use of luminescent micro- and nanomaterials in bioanalytical methods and application of optical sensors in life sciences and oceanography.

**Benoît Fond** is currently research scientist in the Department of Aerodynamism, Aeroelasticity and Acoustics at ONERA, the French Aerospace Lab. His research focuses on the development of quantitative flow imaging techniques for temperature, velocity and pressure. He obtained his Ph.D. in Mechanical Engineering from Imperial College London in 2014 and was from 2016 to 2021, Junior Professor of Experimental Thermofluids at the University of Magdeburg in Germany. He currently holds two research grants from the German Research Foundation (DFG).