From waste to resource: Effects of digested rotten potato supernatant on the growth, total biomass and nutrient composition of Chlorella vulgaris.

A study was carried out to evaluate the growth performance of Chlorella vulgaris, a green microalga, in three different concentrations of digested rotten potato supernatant (DRPS) for 16 days. C. vulgaris was grown in 20 % (T1), 40 % (T2), and 60 % (T3) of the DRPS and at the same time in Bold Basal Medium (BBM) as a control (T4). A significantly highest cell density of C. vulgaris was found in T1 (192.83 ± 1.75 × 105 cells mL-1) in comparison to T2 (136.83 ± 5.58 × 105 cells mL-1), and T3 (99.11 ± 5.38 × 105 cells mL-1) (p < 0.001 for all comparisons) while the cell density at T1 (192.83 ± 1.75 × 105 cells mL-1) and T4 (180.907 ± 4.58 × 105 cells mL-1) did not differ significantly (p = 0.227). Moreover, the mean daily division rate of C. vulgaris was significantly higher in T1 (0.340 ± 0.001 divisions day-1) in comparison to other concentrations of DRPS (p < 0.001 for all comparisons). The maximum value of total biomass (1.07 ± 0.10 g L-1) was found in T1 which was statistically similar to those in T4 and T2. In addition, there was no significant difference between the mean maximum values of chlorophyll-a content and optical density of C. vulgaris in T1 and T4. The highest protein content of 42.67 ± 0.57 % was observed in T4 which was significantly higher than T1 (39.43 ± 1.67 %) (p = 0.027). It is also worth mentioning that there was no significant difference in the crude lipid content of the microalgae grown in T1 (10.06 ± 0.17 %) and T4 (9.88 ± 0.14 %) (p = 0.616). Hence, 20 % DRPS can be used as an alternative culture media of BBM for C. vulgaris with a broad aim to accelerate the sustainable advancement of microalgal production.

Unveiling the potential of a novel portable air quality platform for assessment of fine and coarse particulate matter: in-field testing, calibration, and machine learning insights.

Although low-cost air quality sensors facilitate the implementation of denser air quality monitoring networks, enabling a more realistic assessment of individual exposure to airborne pollutants, their sensitivity to multifaceted field conditions is often overlooked in laboratory testing. This gap was addressed by introducing an in-field calibration and validation of three PAQMON 1.0 mobile sensing low-cost platforms developed at the Mining and Metallurgy Institute in Bor, Republic of Serbia. A configuration tailored for monitoring PM2.5 and PM10 mass concentrations along with meteorological parameters was employed for outdoor measurement campaigns in Bor, spanning heating (HS) and non-heating (NHS) seasons. A statistically significant positive linear correlation between raw PM2.5 and PM10 measurements during both campaigns (R > 0.90, p ≤ 0.001) was observed. Measurements obtained from the uncalibrated NOVA SDS011 sensors integrated into the PAQMON 1.0 platforms exhibited a substantial and statistically significant correlation with the GRIMM EDM180 monitor (R > 0.60, p ≤ 0.001). The calibration models based on linear and Random Forest (RF) regression were compared. RF models provided more accurate descriptions of air quality, with average adjR2 values for air quality variables in the range of 0.70 to 0.80 and average NRMSE values between 0.35 and 0.77. RF-calibrated PAQMON 1.0 platforms displayed divergent levels of accuracy across different pollutant concentration ranges, achieving a data quality objective of 50% during both measurement campaigns. For PM2.5, uncertainty ( U r ) was below 50% for concentrations between 9.06 and 34.99 µg/m3 in HS and 5.75 and 17.58 µg/m3 in NHS, while for PM10, it stayed below 50% from 19.11 to 51.13 µg/m3 in HS and 11.72 to 38.86 µg/m3 in NHS.

Introduction and Validation of Low-Cost Ocular Biometry in Healthy and Cataractous Eyes Using a Thermally-Tunable Swept-Source Laser.

PURPOSE: To introduce and validate a novel substantially lower-priced and rapid swept-source investigational optical biometer in healthy and cataractous eyes, employing a thermally tuned laser diode used extensively in cell-phones and data communication as an alternative swept-source. DESIGN: Prospective accuracy, validity, and reliability analysis. METHODS: Sixty eyes of 59 subjects (twenty-nine eyes of 29 healthy subjects and thirty-one eyes of 30 cataract patients) were enrolled in a prospective comparative study at the Vienna General Hospital between August 2021 and April 2023. Averaged intraocular distances were acquired in 2.5 seconds from datasets consisting of 5000 consecutive A-scans at a single position by a low-cost swept-source optical biometry (SSOB) system. Instrument repeatability was assessed via standard deviations (SDs) and coefficients of variation (COVs) of parameters such as axial length (AL), anterior chamber depth (ACD), lens thickness (LT), and central corneal thickness (CCT). Healthy subjects and cataract patients were subsequently measured on the same day with the SSOB and a referential partial coherence interferometry (PCI) biometer (IOL Master 500, Zeiss, Jena, Germany) to establish AL inter-device correlation (r) for instrument calibration. AL and ACD as shared parameters between both biometers were evaluated for their limits of agreements (LoA) using Bland-Altman analysis. RESULTS: Repeated measurements of AL, ACD, LT, and CCT revealed SDs of 18 µm, 12 µm, 12 µm, and 10 µm, respectively. All parameters except for CCT had a COV < 1%. Except for 1 eye with white cataract, 59 eyes of 59 study participants with various degrees and types of cataract could be measured with both devices. AL inter-device correlation was excellent (r=>0.99). The 95 % LoAs between both biometers were -0.14 to 0.13 mm for AL and -0.28 to 0.25 mm for ACD. CONCLUSIONS: Optical biometry using a thermally-tunable VCSEL swept source light source has the potential to provide clinically relevant biometric parameters at an unprecedented 100-fold lower price point than currently employed state of the art optical biometers, paving the way for compact devices in remote care settings.

Design and characterization of a low-cost particle image velocimetry system.

Particle Image Velocimetry (PIV) is considered the gold standard technique for flow visualization. However, its cost (at least tens of thousands of dollars) can prove inhibitive in its standard form. This article presents an alternative design, leveraging off-the-shelf and open-source options for each key component involved: camera, laser module, optical components, tracer particles, and analysis software. Flow visualization is a crucial technique to connect theory to practice in teaching and researching fluid mechanics. Despite the ubiquity of this field within engineering curricula, many undergraduate institutions globally forego utilizing such equipment, given the barriers to setting it up. The availability of this low-cost alternative (∼$500) that can be built in-house offers a path forward. Characterization was done by visualizing the rotational flow generated by a magnetic stirrer in a cylindrical beaker. The velocity magnitude around the stirrer bar measured by the low-cost PIV system was compared to expected values calculated analytically. The percent difference was between 1-2% when the flow stayed two-dimensional but increased as the flow began developing into more of a 3-D flow. Repeatability varied no more than 6% between experiments. This platform holds the potential for reliable replication across institutions broadly.

A low-cost and open-hardware portable 3-electrode sleep monitoring device.

To continue sleep research activities during the lockdown resulting from the COVID-19 pandemic, experiments that were previously conducted in laboratories were shifted to the homes of volunteers. Furthermore, for extensive data collection, it is necessary to use a large number of portable devices. Hence, to achieve these objectives, we developed a low-cost and open-source portable monitor (PM) device capable of acquiring electroencephalographic (EEG) signals using the popular ESP32 microcontroller. The device operates based on instrumentation amplifiers. It also has a connectivity microcontroller with Wi-Fi and Bluetooth that can be used to stream EEG signals. This portable single-channel 3-electrode EEG device allowed us to record short naps and score different sleep stages, such as wakefulness, non rapid eye movement sleep (NREM), stage 1 (S1), stage 2 (S2), stage 3 (S3) and stage 4 (S4). We validated the device by comparing the obtained signals to those generated by a research-grade counterpart. The results showed a high level of accurate similarity between both devices, demonstrating the feasibility of using this approach for extensive and low-cost data collection of EEG sleep recordings.

Low-cost, reliable, and highly efficient removal of COD and total nitrogen from sewage using a sponge-filled trickling filter.

Development of low-cost and reliable reactors demanding minimal supervision is a need-of-the-hour for sewage treatment in rural areas. This study explores the performance of a multi-stage sponge-filled trickling filter (SPTF) for sewage treatment, employing polyethylene (PE) and polyurethane (PU) media. Chemical oxygen demand (COD) and nitrogen transformation were evaluated at hydraulic loading rates (HLRs) ranging from 2 to 6 m/d using synthetic sewage as influent. At influent COD of ∼350 mg/L, PU-SPTF and PE-SPTF achieved a COD removal of 97% across all HLRs with most of the removal occurring in the first segments. Operation of PE-SPTF at an HLR of 6 m/d caused substantial wash-out of biomass, while PU-SPTF retained biomass and achieved effluent COD < 10 mg/L even at HLR of 8-10 m/d. The maximum Total Nitrogen removal by PE-SPTF and PU-SPTF reactors was 93.56 ± 1.36 and 92.24 ± 0.66%, respectively, at an HLR of 6 m/d. Simultaneous removal of ammonia and nitrate was observed at all the HLRs in the first segment of both SPTFs indicating ANAMMOX activity. COD removal data, media depth, and HLRs were fitted (R2 > 0.99) to a first-order kinetic relationship. For a comparable COD removal, CO2 emission by PU-SPTF was 3.5% of that of an activated sludge system.

Low-cost clamp for the measurement of vegetation spectral signatures.

Spectral signatures allow the characterization of a surface from the reflected or emitted energy along the electromagnetic spectrum. This type of measurement has several potential applications in precision agriculture. However, capturing the spectral signatures of plants requires specialized instruments, either in the field or the laboratory. The cost of these instruments is high, so their incorporation in crop monitoring tasks is not massive, given the low investment in agricultural technology. This paper presents a low-cost clamp to capture spectral leaf signatures in the laboratory and the field. The clamp can be 3D printed using PLA (polylactic acid); it allows the connection of 2 optical fibers: one for a spectrometer and one for a light source. It is designed for ease of use and holds a leave firmly without causing damage, allowing data to be collected with less disturbance. The article compares signatures captured directly using a fiber and the proposed clamp; noise reduction across the spectrum is achieved with the clamp.

Introducing PLEMS: the application of a low-cost, portable monitoring system in environmental walks.

The application of innovative systems using low-cost microcontrollers in human biometeorology studies is a promising alternative to conventional monitoring devices, which are usually cost-intensive and provide measurements at specific points, as in stationary meteorological stations. A Portable Low-cost Environmental Monitoring System (PLEMS) aimed at the pedestrian scale is introduced. The backpack-type equipment consists of a microcontroller with attached sensors that assess environmental conditions in a broad sense, integrating measurements of air quality, lighting and noise levels alongside variables typically measured at meteorological stations. The application of the system took place in altogether 12 environmental walks carried out with questionnaire-surveys with concurrent environmental monitoring with the PLEMS in Curitiba, Brazil, a subtropical location characterized by a Cfb climate type. Results allowed us to test the equipment and method of data gathering within a limited period (approximately 50 min) and for a short walking circuit of 800 m. The equipment was successfully able to capture even slightest differences in environmental conditions among points of interest, whereas subjective responses (n= 3843 responses to a total of 11 questions) showed consistency with measured data. From a multi-domain perspective, relevant insights could be obtained for the measured variables.

Assessing bias in personal exposure estimates when indoor air quality is ignored: A comparison between GPS-enabled mobile air sensor data and stationary outdoor sensor data.

Neglecting indoor air quality in exposure assessments may lead to biased exposure estimates and erroneous conclusions about the health impacts of exposure and environmental health disparities. This study assessed these biases by comparing two types of personal exposure estimates for 100 individuals: one derived from real-time particulate matter (PM2.5) measurements collected both indoors and outdoors using a low-cost portable air monitor (GeoAir2.0) and the other from PurpleAir sensor network data collected exclusively outdoors. The PurpleAir measurement data were used to create smooth air pollution surfaces using geostatistical methods. To obtain mobility-based exposure estimates, both sets of air pollution data were combined with the individuals' GPS tracking data. Paired-sample t-tests were then performed to examine the differences between these two estimates. This study also investigated whether GeoAir2.0- and PurpleAir-based estimates yielded consistent conclusions about gender and economic disparities in exposure by performing Welch's t-tests and ANOVAs and comparing their t-values and F-values. The study revealed significant discrepancies between GeoAir2.0- and PurpleAir-based estimates, with PurpleAir data consistently overestimating exposure (t = 5.94; p < 0.001). It also found that females displayed a higher average exposure than males (15.65 versus. 8.55 µg/m3) according to GeoAir2.0 data (t = 4.654; p = 0.055), potentially due to greater time spent indoors engaging in pollution-generating activities traditionally associated with females, such as cooking. This contrasted with the PurpleAir data, which indicated higher exposure for males (43.78 versus. 46.26 µg/m3) (t = 3.793; p = 0.821). Additionally, GeoAir2.0 data revealed significant economic disparities (F = 7.512; p < 0.002), with lower-income groups experiencing higher exposure-a disparity not captured by PurpleAir data (F = 0.756; p < 0.474). These findings highlight the importance of considering both indoor and outdoor air quality to reduce bias in exposure estimates and more accurately represent environmental disparities.

Innovative optimization for enhancing Pb2+ biosorption from aqueous solutions using Bacillus subtilis.

Introduction: Toxic heavy metal pollution has been considered a major ecosystem pollution source. Unceasing or rare performance of Pb2+ to the surrounding environment causes damage to the kidney, nervous, and liver systems. Microbial remediation has acquired prominence in recent decades due to its high efficiency, environment-friendliness, and cost-effectiveness. Methods: The lead biosorption by Bacillus subtilis was optimized by two successive paradigms, namely, a definitive screening design (DSD) and an artificial neural network (ANN), to maximize the sorption process. Results: Five physicochemical variables showed a significant influence (p < 0.05) on the Pb2+ biosorption with optimal levels of pH 6.1, temperature 30°C, glucose 1.5%, yeast extract 1.7%, and MgSO4.7H2O 0.2, resulting in a 96.12% removal rate. The Pb2+ biosorption mechanism using B. subtilis biomass was investigated by performing several analyses before and after Pb2+ biosorption. The maximum Pb2+ biosorption capacity of B. subtilis was 61.8 mg/g at a 0.3 g biosorbent dose, pH 6.0, temperature 30°C, and contact time 60 min. Langmuir's isotherm and pseudo-second-order model with R2 of 0.991 and 0.999 were suitable for the biosorption data, predicting a monolayer adsorption and chemisorption mechanism, respectively. Discussion: The outcome of the present research seems to be a first attempt to apply intelligence paradigms in the optimization of low-cost Pb2+ biosorption using B. subtilis biomass, justifying their promising application for enhancing the removal efficiency of heavy metal ions using biosorbents from contaminated aqueous systems.

The Perry project: A versatile low-cost syringe pump for dispensing in automated synthesis systems.

Syringe pumps are used in applications such as chemistry, medicine, or microbiology offering high-precision dosing or a way to ease the workload of tedious tasks. Further, high-performance syringe pumps are crucial to automating laboratory tasks. The Perry Pump is demonstrated with syringe sizes ranging from 1-20 mL and includes a reservoir, which enables larger volumes to be used. The lowest volume demonstrated is 20 µ L at 5.5 µ L/s, while the largest is 20 mL at 0.145 mL/s. The Perry Pump is designed with the intention of easy to 3D-print, limited metal parts, and a high versatility and tolerance to chemicals.

Influence of Fix Schedule on the Location Accuracy of a Low-Cost GPS Data Logger on Cattle.

Global positioning system (GPS) data loggers are commonly used to track the movements and distribution of both wild and domestic animals. However, the expense often poses a challenge for researchers. Recently, there has been a rise in the utilization of affordable and user-friendly GPS data loggers for tracking animal movements, albeit with compromised accuracy. We aimed to identify factors influencing the accuracy of a low-cost GPS data logger (I-gotU GT-600) and to enhance its location accuracy. Initial investigations revealed that recording intervals impacted the location error of the GPS data logger. To elucidate the relationship between recording intervals and location accuracy, we conducted stationary and motion tests. Our findings indicated that recording intervals of less than 15 sec substantially enhances the location accuracy of the low-cost GPS data logger. Our results highlight the relationship between the fix schedule and location accuracy for these GPS data loggers. Our study provides information that enhances the quality of data for researchers using low-cost GPS data loggers for short-term studies in various settings, such as zoos and livestock facilities.

Insight mechanism of magnetic activated catalyst derived from recycled steel residue for black liquor degradation.

The present work deals with developing a method for revalorizing steel residues to create sunlight-active photocatalysts based on iron oxides. Commercial-grade steel leftovers are oxidized under different combinations of pH and temperature (50-90 °C and 3 ≥ pH ≤ 5) in a low energy-intensive setup. The material with the highest production efficiency (yield > 12%) and magnetic susceptibility (χm = 387 × 10-6 m3/kg) was further explored and modified by diffusion of M2+ (Zn and Co) ions within the structure of the oxide using a hydrothermal method to create ZnFe2O4, CoFe2O4 and combined Co-Zn ferrite. (Co-Zn)Fe2O4 displayed a bandgap of 2.02 eV and can be activated under sunlight irradiation. Electron microscopy studies show that (Co-Zn)Fe2O4 consists of particles with diameters between 400 and 700 nm, homogeneous size, even distribution, and good dispersibility. Application of the developed materials in the sunlight catalysis of black liquors from cellulose extraction resulted in a reduction of the Chemical Oxygen Demand (- 15% on average) and an enhancement in biodegradability (> 0.57 BOD/COD) after 180 min of reaction. Since the presented process employs direct solar light, it opens the possibility to large-scale water treatment and chemical upgrading applications.

The Use of Breast-specific Gamma Imaging as a Low-Cost Problem-Solving Strategy for Avoiding Biopsies in Patients With Inconclusive Imaging Findings on Mammography and Ultrasonography.

OBJECTIVE: To evaluate the clinical performance and financial costs of breast-specific gamma imaging (BSGI) as a biopsy-reducing problem-solving strategy in patients with inconclusive diagnostic imaging findings. METHODS: A retrospective analysis of all patients for whom BSGI was utilized for inconclusive imaging findings following complete diagnostic mammographic and sonographic evaluation between January 2013 and December 2018 was performed. Positive BSGI findings were correlated and biopsied with either US or stereotactic technique with confirmation by clip location and pathology. After a negative BSGI result, patients were followed for a minimum of 24 months or considered lost to follow-up and excluded (22 patients). Results of further imaging studies, biopsies, and pathology results were analyzed. Net savings of avoided biopsies were calculated based on average Medicare charges. RESULTS: Four hundred and forty female patients from 30 to 95 years (mean 55 years) of age were included in our study. BSGI demonstrated a negative predictive value (NPV) of 98.4% (314/319) and a positive predictive value for biopsy of 35.5% (43/121). The overall sensitivity was 89.6% (43/48), and the specificity was 80.1% (314/392). In total, 78 false positive but only 5 false negative BSGI findings were identified. Six hundred and twenty-one inconclusive imaging findings were analyzed with BSGI and a total of 309 biopsies were avoided. Estimated net financial savings from avoided biopsies were $646 897. CONCLUSION: In the management of patients with inconclusive imaging findings on mammography or ultrasonography, BSGI is a problem-solving imaging modality with high NPV that helps avoid costs of image-guided biopsies.

Single-arm, first-in-human feasibility study results for an ultra-low-cost insulin pump.

BACKGROUND: Use of Continuous Subcutaneous Insulin Infusion (CSII) has been shown to improve glycemic outcomes in Type 1 Diabetes (T1D), but high costs limit accessibility. To address this issue, an inter-operable, open-source Ultra-Low-Cost Insulin Pump (ULCIP) was developed and previously shown to demonstrate comparable delivery accuracy to commercial models in standardised laboratory tests. This study aims to evaluate the updated ULCIP in-vivo, assessing its viability as an affordable alternative for those who cannot afford commercially available devices. METHODS: This first-in-human feasibility study recruited six participants with T1D. During a nine-hour inpatient stay, participants used the ULCIP under clinical supervision. Venous glucose, insulin, and ß-Hydroxybutyrate were monitored to assess device performance. RESULTS: Participants displayed expected blood glucose and blood insulin levels in response to programmed basal and bolus insulin dosing. One participant developed mild ketosis, which was treated and did not recur when a new pump reservoir was placed. All other participants maintained ß-Hydroxybutyrate < 0.6 mmol/L throughout. CONCLUSION: The ULCIP safely delivered insulin therapy to users in a supervised inpatient environment. Future work should focus on correcting a pump hardware issue identified in this trial and extending device capabilities for use in closed loop control. Longer-term outpatient studies are warranted. TRIAL REGISTRATION: The trial was prospectively registered with the Australian New Zealand Clinical Trials Registry (ACTRN12623001288617) on the 11 December 2023.

HRV and EEG correlates of well-being using ultra-short, portable, and low-cost measurements.

Wearable electroencephalography (EEG) and electrocardiography (ECG) devices may offer a non-invasive, user-friendly, and cost-effective approach for assessing well-being (WB) in real-world settings. However, challenges remain in dealing with signal artifacts (such as environmental noise and movements) and identifying robust biomarkers. We evaluated the feasibility of using portable hardware to identify potential EEG and heart-rate variability (HRV) correlates of WB. We collected simultaneous ultrashort (2-min) EEG and ECG data from 60 individuals in real-world settings using a wrist ECG electrode connected to a 4-channel wearable EEG headset. These data were processed, assessed for signal quality, and analyzed using the open-source EEGLAB BrainBeats plugin to extract several theory-driven metrics as potential correlates of WB. Namely, the individual alpha frequency (IAF), frontal and posterior alpha asymmetry, and signal entropy for EEG. SDNN, the low/high frequency (LF/HF) ratio, the Poincaré SD1/SD2 ratio, and signal entropy for HRV. We assessed potential associations between these features and the main WB dimensions (hedonic, eudaimonic, global, physical, and social) implementing a pairwise correlation approach, robust Spearman's correlations, and corrections for multiple comparisons. Only eight files showed poor signal quality and were excluded from the analysis. Eudaimonic (psychological) WB was positively correlated with SDNN and the LF/HF ratio. EEG posterior alpha asymmetry was positively correlated with Physical WB (i.e., sleep and pain levels). No relationships were found with the other metrics, or between EEG and HRV metrics. These physiological metrics enable a quick, objective assessment of well-being in real-world settings using scalable, user-friendly tools.

Developing a low-cost, open-source, locally manufactured workstation and computational pipeline for automated histopathology evaluation using deep learning.

BACKGROUND: Deployment and access to state-of-the-art precision medicine technologies remains a fundamental challenge in providing equitable global cancer care in low-resource settings. The expansion of digital pathology in recent years and its potential interface with diagnostic artificial intelligence algorithms provides an opportunity to democratize access to personalized medicine. Current digital pathology workstations, however, cost thousands to hundreds of thousands of dollars. As cancer incidence rises in many low- and middle-income countries, the validation and implementation of low-cost automated diagnostic tools will be crucial to helping healthcare providers manage the growing burden of cancer. METHODS: Here we describe a low-cost ($230) workstation for digital slide capture and computational analysis composed of open-source components. We analyze the predictive performance of deep learning models when they are used to evaluate pathology images captured using this open-source workstation versus images captured using common, significantly more expensive hardware. Validation studies assessed model performance on three distinct datasets and predictive models: head and neck squamous cell carcinoma (HPV positive versus HPV negative), lung cancer (adenocarcinoma versus squamous cell carcinoma), and breast cancer (invasive ductal carcinoma versus invasive lobular carcinoma). FINDINGS: When compared to traditional pathology image capture methods, low-cost digital slide capture and analysis with the open-source workstation, including the low-cost microscope device, was associated with model performance of comparable accuracy for breast, lung, and HNSCC classification. At the patient level of analysis, AUROC was 0.84 for HNSCC HPV status prediction, 1.0 for lung cancer subtype prediction, and 0.80 for breast cancer classification. INTERPRETATION: Our ability to maintain model performance despite decreased image quality and low-power computational hardware demonstrates that it is feasible to massively reduce costs associated with deploying deep learning models for digital pathology applications. Improving access to cutting-edge diagnostic tools may provide an avenue for reducing disparities in cancer care between high- and low-income regions. FUNDING: Funding for this project including personnel support was provided via grants from NIH/NCIR25-CA240134, NIH/NCIU01-CA243075, NIH/NIDCRR56-DE030958, NIH/NCIR01-CA276652, NIH/NCIK08-CA283261, NIH/NCI-SOAR25CA240134, SU2C (Stand Up to Cancer) Fanconi Anemia Research Fund - Farrah Fawcett Foundation Head and Neck Cancer Research Team Grant, and the European UnionHorizon Program (I3LUNG).

The Effect of Component Defects on the Performance of Perovskite Devices and the Low-Cost Preparation of High-Purity PbI2.

The efficiency and reproducibility of perovskite solar cells (PSCs) are significantly influenced by the purity of lead iodide (PbI2) in the raw materials used. Pb(OH)I has been identified as the primary impurity generated from PbI2 in water-based synthesis. Consequently, a comprehensive investigation into the impact of Pb(OH)I impurities on film and device performance is essential. In this study, PbI2, with varying stoichiometries, was synthesized to examine the effects of different Pb(OH)I levels on perovskite device performance. The characterization results revealed that even trace amounts of Pb(OH)I impede the formation of precursor prenucleation clusters. These impurities also increase the energy barrier of the α-phase and facilitate the transition of the intermediate phase to the δ-phase. These effects result in poor perovskite film morphology and sub-optimal photovoltaic device performance. To address these issues, a cost-effective method for preparing high-stoichiometry PbI2 was developed. The formation of Pb(OH)I was effectively inhibited through several strategies: adjusting solution pH and temperature, modifying material addition order, simplifying the precipitation-recrystallization process, and introducing H3PO2 as an additive. These modifications enabled the one-step synthesis of high-purity PbI2. PSCs prepared using this newly synthesized high-stoichiometry PbI2 demonstrated photovoltaic performance comparable to those fabricated with commercial PbI2 (purity ≥ 99.999%). Our novel method offers a cost-effective alternative for synthesizing high-stoichiometry PbI2, thereby providing a viable option for the production of high-performance PSCs.

Fully autonomous water monitoring by plant-inspired robots.

Developing countries struggle with water quality management owing to poor infrastructure, limited expertise, and financial constraints. Traditional water testing, relying on periodic site visits and manual sampling, is impractical for continuous wide-area monitoring and fails to detect sudden heavy metal contamination. To address this, plant-inspired robots capable of fully autonomous water quality monitoring are proposed. Constructed from paper, the robot absorbs surrounding water through its roots. This paper robot is controlled by paper-based microfluidic logic that sends absorbed water to petal-shaped actuators only when the water is polluted by heavy metals. This triggers the actuators to swell and bend like a blooming flower, visually signaling contamination to local residents. In tests with copper-contaminated water, the robotic flower's diameter increased from 4.69 cm to 14.89 cm, a more than threefold expansion (217.25 %). This significant blooming movement serves as a highly visible and easily recognizable indicator of water pollution, even for the public. Furthermore, the paper robot can be mass-produced at a low cost (∼$0.2 per unit) and deployed over large areas. Once installed, the paper robot operates autonomously using surrounding water as a power source, eliminating the need for external electrical infrastructure and expert intervention. Therefore, this autonomous robot offers a new approach to water quality monitoring suitable for resource-limited environments, such as Sub-Saharan Africa.

Impacts of daily household activities on indoor particulate and NO2 concentrations; a case study from oxford UK.

This study explores indoor air pollutant (PM1, PM2.5 and NO2) concentrations over a 15-week period during the COVID-19 pandemic in a typical suburban household in Oxford, UK. A multi-room intensive monitoring study was conducted in a single dwelling using 10 air quality sensors measuring real-time pollutant concentrations at 10 second intervals to assess temporal and spatial variability in PM1, PM2.5 and NO2 concentrations, identify pollution-prone areas, and investigate the impact of residents' activities on indoor air quality. Significant spatial variations in PM concentrations were observed within the study dwelling, with highest hourly concentrations (769.0 & 300.9 µg m-3 for PM2.5, and PM1, respectively) observed in the upstairs study room, which had poor ventilation. Cooking activities were identified as a major contributor to indoor particulate pollution, with peak concentrations aligning with cooking events. Indoor NO2 levels were typically higher than outdoor levels, particularly in the kitchen where a gas-cooking appliance was used. There was no significant association observed between outdoor and indoor PM concentrations; however, a clear correlation was evident between kitchen PM emissions and indoor levels. Similarly, outdoor NO2 had a limited influence on indoor air quality compared to kitchen activities. Indoor sources were found to dominate for both PM and NO2, with higher Indoor/Outdoor (I/O) ratios observed in the upstairs bedroom and the kitchen. Overall, our findings highlight the contribution of indoor air pollutant sources and domestic activities to indoor air pollution exposure, notably during the COVID-19 pandemic when people were typically spending more time in domestic settings. Our novel findings, which suggest high levels of pollutant concentrations in upstairs (first floor) rooms, underscore the necessity for targeted interventions. These interventions include the implementation of source control measures, effective ventilation strategies and occupant education for behaviour change, all aimed at improving indoor air quality and promoting healthier living environments.