Food quality assessment is becoming a global priority due to population growth and the rise of ionic pollutants derived from anthropogenic sources. However, the current methods used to quantify toxic ions are expensive and their operation is complex. Consequently, there is a need for affordable and accessible methods for the accurate determination of ion concentrations in food. Electrochemical sensors based on potentiometry represent a promising approach in this field, with the potential to overcome limitations of the currently available systems. This review summarizes the current advances in the electrochemical quantification of heavy metals and toxic anions in the food industry using potentiometric sensors. The healthcare impact of common heavy metal contaminants (Cd²⁺, Hg²⁺, Pb²⁺, As³⁺) and anions (ClO₄⁻, F⁻, HPO₄⁻, SO₄²⁻, NO₃⁻, NO₂⁻) is discussed, alongside current regulations, and gold standard methods for analysis. Sensor performances are compared to current benchmarks in terms of selectivity and the limit of detection. Given the complexity of food samples, the percentage recovery values (%) and the methodologies employed for ion extraction are also described. Finally, a summary of the challenges and future directions of the field is provided. An overview of technologies that can overcome the limitations of current electrochemical sensors is shown, including new extraction methods for ions in food.

The pharmaceutical industry faces mounting pressure to reduce its environmental impact while maintaining innovation in drug development. Artificial intelligence (AI) has emerged as a transformative tool across healthcare and drug discovery, yet its potential to drive sustainability by improving molecular design remains underexplored. This review critically examines the applications of AI in molecular design that can support in advancing greener pharmaceutical practices across the entire drug life cycle—from design and synthesis to waste management and solvent optimisation. We explore how AI-driven models are being used to personalise dosing, reduce pharmaceutical waste, and design biodegradable drugs with enhanced environmental compatibility. Significant advances have also been made in the predictive modelling of pharmacokinetics, drug–polymer interactions, and polymer biodegradability. AI’s role in the synthesis of active pharmaceutical compounds, including catalysts, enzymes, solvents, and synthesis pathways, is also examined. We highlight recent breakthroughs in protein engineering, biocatalyst stability, and heterogeneous catalyst screening using generative and language models. This review also explores opportunities and limitations in the field. Despite progress, several limitations constrain impact. Many AI models are trained on small or inconsistent datasets or rely on computationally intensive inputs that limit scalability. Moreover, a lack of standardised performance metrics and life cycle assessments prevents the robust evaluation of AI’s true environmental benefits. In particular, the environmental impact of AI-driven molecules and synthesis pathways remains poorly quantified due to limited data on emissions, waste, and energy usage at the compound level. Finally, a summary of challenges and future directions in the field is provided.

The rising population and the ongoing climate crisis call for improved means to monitor and optimise agriculture. A promising approach to tackle current challenges in food production is the early diagnosis of plant diseases through non-invasive methods, such as the detection of volatiles. However, current devices for detection of multiple volatiles are based on electronic noses, which are expensive, require complex circuit assembly, may involve metal oxides with heating elements, and cannot easily be adapted for some applications that require miniaturisation or limit front-end use of electronic components. To address these challenges, a low-cost optoelectronic nose using chemo-responsive colorimetric dyes drop-casted onto filter paper has been developed in the current work. The final sensors could be used for the quantitative detection of up to six plant volatiles through changes in colour intensities with a sub-ppm level limit of detection, one of the lowest limits of detection reported so far using colorimetric gas sensors. Sensor colouration could be analysed using a low-cost spectrometer and the results could be processed using a microcontroller. The measured volatiles could be used for the early detection of plant abiotic stress as early as two days after exposure to two different stresses: high salinity and starvation. This approach allowed a lowering of costs to GBP 1 per diagnostic sensing paper. Furthermore, the small size of the paper sensors allows for their use in confined settings, such as Petri dishes. This detection of abiotic stress could be easily achieved by exposing the devices to living plants for 1 h. This technology has the potential to be used for monitoring of plant development in field applications, early recognition of stress, implementation of preventative measures, and mitigation of harvest losses.

The development of sensing devices for precision agriculture is crucial to boost crop yields and limit shortages in food productions due to the growing population. However, current approaches cannot provide direct information about the physiological status of the plants, reducing sensing accuracy. The development of implanted devices for plant monitoring represents a step forward in this field, enabling the direct assessment of key biomarkers in plants. However, available devices are expensive and cannot be used for long-term applications. The current work presents the application of ruthenium oxide-based nanofilms for the in vivo monitoring of pH in plants. The sensors were manufactured using the low-cost electrodeposition of RuO₂ films, and the final device could be successfully incorporated for the monitoring of xylem sap pH for at least 10 h. RuO₂ nanoparticles were chosen as the sensing material due to its biocompatibility and chemical stability. To reduce the noise rates and drift of the sensors, a protective layer consisting of a cellulose/PDMS hybrid material was deposited by an aerosol method (>GBP 50), involving off-the-shelf devices, leading to a good control of film thickness. Nanometrically thin films with a thickness of 80 nm and roughness below 3 nm were fabricated. This film led to a seven-fold decrease in drift while preserving the selectivity of the sensors towards H⁺ ions. The sensing devices were tested in vivo by implantation inside a tomato plant. Environmental parameters such as humidity and temperature were additionally monitored using a low-cost Wio Terminal device, and the data were sent wirelessly to an online server. The interactions between plant tissues and metal oxide-based sensors were finally studied, evidencing the formation of a lignified layer between the sensing film and xylem. Thus, this work reports for the first time a low-cost electrochemical sensor that can be used for the continuous monitoring of pH in xylem sap. This device can be easily modified to improve the long-term performance when implanted inside plant tissues, representing a step forward in the development of precision agriculture technologies.