This study assesses the bioenergy potential of two types of aquatic biomass found in the Republic of Congo: the green macroalgae Ulva lactuca (UL) and Ledermanniella schlechteri (LS). Their combustion behaviour was assessed using elemental and biochemical analysis, TGA, bomb calorimetry and metal analysis. Their anaerobic digestion behaviour was determined using biochemical methane potential (BMP) tests. The average HHV for LS is 14.1 MJ kg⁻¹, whereas UL is lower (10.5 MJ kg⁻¹). Both biomasses have high ash contents and would be problematic during thermal conversion due to unfavourable ash behaviour. Biochemical analysis indicated high levels of carbohydrate and protein and low levels of lipids and lignin. Although the lipid profile is desirable for biodiesel production, the levels are too low for feasible extraction. High levels of carbohydrates and protein make both biomasses suitable for anaerobic digestion. BMP tests showed that LS and UL have an average of 262 and 161 mL CH₄ gVS⁻¹, respectively. The biodegradability (BI) of LS and UL had an average value of 76.5% and 43.5%, respectively. The analysis indicated that these aquatic biomasses are unsuitable for thermal conversion and lipid extraction; however, conversion through anaerobic digestion is promising.

High-density monolayers (HDMs) of single-strand (ss) DNA are important nanoscale platforms for the fabrication of sensors and for mechanistic studies of enzymes on surfaces. Such systems can be used, for example, to monitor gene expression, and for the construction of more complex nanodevices via selective hybridization with the complementary oligos dissolved in solution. In this framework, controlling HDM hybridization is essential to control the final properties. Different studies demonstrate that at the typical density of ≈10¹³ molecules per cm² no more than ≈30–40% of the HDM ssDNA is successfully hybridized. Until now, however, the origin of the HDM hybridization limit has remained unclear. In this work, molecular dynamics (MD) simulations of HDM systems with variable hybridization reveal that, independently of other experimental parameters, the effective hybridization for a HDM of this density is intrinsically limited by molecular and electrostatic crowding. A detailed structural analysis of the HDM model shows good agreement with our atomic force microscopy (AFM) experiments, and provides further insight into the steric hindrance behaviour and time-resolved surface topography of these nanostructured systems. The explicit relationship proposed between structural crowding and limited HDM hybridization offers a rationale to control the final properties of HDM-based nanodevices.

Single-stranded DNA (ssDNA) brushes, in which ssDNA oligomers are tethered to surfaces in dense monolayers, are being investigated for potential biosensing applications.

This study assesses and characterizes six woody biomass (WB) species commonly harvested in the Republic of Congo: Millettia laurentii (WB1), Millettia eetveldeana (WB2), Hymenocardia ulmoides (WB3), Markhamia tomentosa (WB4), Pentaclethra eetveldeana (WB5), and Hymenocardia acida (WB6). Characterization was performed using proximate analysis with a Thermo Gravimetric Analyser (TGA), ultimate analysis with a CHNS Analyser, higher heating value (HHV) determination, metal content analysis by X-ray fluorescence (XRF), and aboveground biomass (AGB) estimation. The proximate analysis results showed that volatile matter varied between 74.6% and 77.3%, while the ultimate analysis indicated that carbon content ranged from 43% to 46%, with low nitrogen content. XRF analysis revealed low levels of heavy metals in all samples. The HHV results, using three models (Dulong’s equation, Friedl, and proximate analysis), showed higher values with Friedl’s method (17.3–18.2 MJ/kg) and proximate analysis (15.26–19.23 MJ/kg) compared to Dulong’s equation (13.9–14.9 MJ/kg). Savannah biomass (WB6) exhibited high AGB (7.28 t), 14.55 t/ha, and carbon stock (7.28 t). Compared to forest biomass, savannah biomass presents a higher potential for bioenergy production. Minimal statistical analysis of wood biomass showed that parameters such as volatile matter (VM), carbon (C), hydrogen (H), and calculated HHV have low variability, suggesting the biomass is relatively homogeneous. However, moisture and nitrogen showed significant standard deviations, indicating variability in storage conditions or sample nature. Statistical analysis of forest biomass estimation revealed different mean values for diameter, AGB (t and t/ha), and carbon stock, with high standard deviations, indicating a heterogeneous forest with both young and mature trees. These analyses and estimates indicate that these WB species are suitable for biofuel and bioenergy production using gasification, pyrolysis, and combustion processes. Among these thermochemical processes, gasification is the most efficient compared to combustion and pyrolysis.

Installing Photovoltaic (PV) systems is becoming a viable solution for rural areas, with an innovative approach consisting of interconnecting autonomous PV-powered households. This study analyses the performance of interconnected autonomous PV households under different weather conditions using the Matlab Simulink R2016a software. Over and above this interconnection analysis, in this study, a new mathematical model to predict PV power is also proposed and compared to the simulation's results and other approaches allowing to prosumer to control their interconnection to others. The interconnection demonstrated the improving performance of microgrids and mutual energy compensation, increasing efficiency by 9% in houses with a deficit. To manage the energy flow of these autonomous houses, the households' energy management is independent and the bus Voltage (Vdc) is maintained at around 52V, corresponding to the maximum bus voltage setpoint, thanks to integrated Proportional control (PI). The results of the simulation of the interconnection using the Matlab Simulink R2016a software show that irradiation and temperature have an impact on photovoltaic production and the results of the new mathematical approach are approximately 2% of those of the simulations.

The Republic of the Congo is facing significant challenges in terms of access to electricity, particularly in the extensive rural and remote areas. A reliable assessment of solar resources and photovoltaic (PV) potential is needed to support the development of decentralized sustainable energy solutions. This study evaluates the potential for photovoltaic electricity production in Brazzaville based on in situ radiation measurements. The potential was extended across the country using satellite data from SolarGIS through calibration, although this requires further validation of satellite data in certain transition zones, ranging from the southern savannas to the dense equatorial forests in the north of the country. Measurements taken over a year at the Cité Scientifique de Brazzaville using LP02 and CS300 pyranometers were compared to SolarGIS estimates using statistical performance indicators. There is better agreement between these sensors, with R² = 0.80 and an RMSE = 0.47 kWh/m²/day. However, we observed between the SolarGIS data and the LP02 measurements an R² = 0.91 and an RMSE = 0.17 kWh/m²/day (3.8%), whereas with the CS300 we obtained R² = 0.77 and RMSE = 0.59 kWh/m²/day (14%), which shows a better performance of LP02 compared with CS300. In terms of photovoltaic energy production, the average photovoltaic solar yield was estimated at 3.9 kWh/kWp/day for the LP02 and 3.5 kWh/kWp/day for the CS300, with an annual yield of approximately 1424 kWh/kWp for the LP02, corresponding to estimated discrepancies in photovoltaic production of 0.14 kWh/kWp/day. Based on this, we estimated that a 50 kWp photovoltaic solar system would produce approximately an annual power of 71,200 kWh, which would supply 285 households for a village. These results confirm both the significant potential of solar photovoltaics for decentralized rural electrification and the usefulness of calibrated SolarGIS data for assessing solar resources in areas with limited site-level data. These efforts align well with the planning and investment needs deployed by the Republic of Congo to ensure sustainable electrification of rural areas (PND 2022–2026; National Energy Pact (NEP) and Mission 300).