The extensive use of pesticides requires innovative approaches to remove these compounds from the environment. Carbon materials are traditionally used as adsorbents for removing pesticides, and the development of the new sorts of carbon materials allows more advanced approaches in environmental applications. Using density functional calculations, we have predicted chemical reaction between the S(O)=P moieties of the organophosphates with point defects in graphene ? single vacancies, Stone?Wales defects and epoxy- -groups. The reaction was confirmed using ultra high performance chromatography for two graphene oxide samples and dimethoate as a representative of organophosphates. The exact reaction mechanism is still elusive, but it is unambiguously confirmed that no selective oxidation of dimethoate to more toxic oxo-analog occurs. The presented results can help to develop novel systems for the irreversible conversion of organophosphates to non-toxic compounds, without using aggressive chemical agents or external physical factors like UV radiation.

Laboratory exercises are an important part of the teaching process at many faculties. However, despite the fact that today the implementation of laboratory exercises is carried out on modern equipment, the full potential of that equipment is often not used, that is, often some of the possibilities of modern equipment are not fully used in student practice. In the paper, it will be shown on two cases from practice how the student user experience can be expanded by using exactly those „forgotten“ options. In the first case, a solution based on the full use of Standard Commands for Programmable Instruments (SCPI), which are implemented on most modern measuring devices, will be presented. The second case will consider the application of open source hardware and software supported by LoRa connectivity, which enables the expansion of the spectrum of exercises performed by students. It will be shown that both solutions can be implemented without a high level of complexity and large financial efforts for the higher education institution itself.

This paper presents a real-world case of using open data to improve learning outcomes in the education of future graduate engineers based on the use of open data in synergy with an open API to collect real-time data. This enables students to acquire knowledge based on real-world data rather than relying on artificially generated datasets. Such an approach allows students to encounter real-life examples during their studies and to become familiar with the entire process - from data collection at the source, through storage, filtering, and other processing steps, to obtaining meaningful and usable final results.

A dual-modification strategy integrating hydrogenation with plasma-enhanced atomic layer deposition (PE-ALD) is developed to address bulk carrier recombination and sluggish the OER kinetics in TiO₂ photoanodes. Oxygen vacancies (Ov) introduced by hydrogenation broaden light response and suppress bulk charge recombination, while ALD-deposited Co nanoparticles form a Schottky junctions serving as hole-trapping centers, reducing OER overpotential and extending carrier lifetime. The optimized TNB-H2@Co-200 photoanode achieves a photocurrent density of 1.02 mA cm⁻² at 1.23 V vs. RHE under AM 1.5G illumination — about five times that of pristine TiO₂ — demonstrating outstanding PEC performance.

Reliable transition of laboratory-developed gas-sensing nanomaterials into environmental monitoring systems remains limited by the lack of integrated testing platforms capable of reproducing realistic operating conditions while providing automated, high-quality sensor characterization. In this work, we present a comprehensive framework for environmental gas sensor development centered on a custom-designed gas sensor chamber (GSC) and its connection to standalone measuring stations (sMSs) intended for smart city air quality monitoring. The GSC was developed as a dedicated platform for controlled evaluation of sensing nanomaterials through automated regulation of temperature, relative humidity, gas concentration, and pressure, combined with impedance measurements over a broad frequency range (10 Hz–100 kHz). The system integrates dedicated chamber hardware, environmental control loops, an LCR/impedance-based measurement unit, and in-house software for instrument control, data acquisition, synchronization of environmental and impedance data, and post-processing with visualization and correlation analysis. The functionality of the platform was demonstrated using graphene oxide-based nanocomposites deposited on interdigital electrodes and tested under variable parameters of temperature, humidity, and carbon monoxide concentration. The chamber enabled stable and reproducible extraction of frequency-dependent sensor responses and identification of optimal operating conditions for selected materials. Building on these results, the same sensing concept was translated into an energy-autonomous sMS architecture based on modular interdigital sensor elements, impedance-based indirect gas quantification, remote communication, and photovoltaic power support. By integrating material evaluation, environmental simulation, automated data handling, and field-oriented station design within a single workflow, the presented approach bridges the gap between laboratory gas sensor research and practical deployment in distributed smart city monitoring networks.

The standard practice in calculation and laboratory exercises often involves students working with the same dataset. While this simplifies the process, it also creates a significant opportunity for academic dishonesty, such as plagiarism, as students may resort to using others’ work or results. This undermines the intended learning outcomes. Additionally, there is tendency of the use of measurement data from real world which presents some challenges, particularly in terms of providing a large volume of diverse datasets due to limitations in time, funding, resources, and the accuracy and repeatability of measurements. This paper introduces a new approach that leverages machine learning and predictive analytics to address these issues. Starting with an initial real dataset obtained through experimental methods, different models are used to predict results, generating new synthetic datasets for educational purposes. By varying the models and their parameters, multiple versions of the data are created, ensuring that smaller student groups receive identical datasets for their tasks, while in some cases, each student may be assigned a unique dataset. The paper also has an evaluation of the results achieved through this approach in teaching both basic and master’s level courses in a one technical faculty’s study program.