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Diversity of biomass usage pathways to achieve emissions targets in the European energy system

Biomass is a versatile renewable energy source with applications across the energy system, but it is a limited resource and its usage needs prioritization. We use a sector-coupled European energy system model to explore near-optimal solutions for achieving emissions targets. We find that provision of biogenic carbon has higher value than bioenergy provision. Energy system costs increase by 20% if biomass is excluded at a net-negative (−110%) emissions target and by 14% at a net-zero target. Dispatchable bioelectricity covering ~1% of total electricity generation strengthens supply reliability. Otherwise, it is not crucial in which sector biomass is used, if combined with carbon capture to enable negative emissions and feedstock for e-fuel production. A shortage of renewable electricity or hydrogen supply primarily increases the value of using biomass for fuel production. Results are sensitive to upstream emissions of biomass, carbon sequestration capacity and costs of direct air capture.

Microwave catalytic pyrolysis of biomass: a review focusing on absorbents and catalysts

The quest for low-carbon alternatives to fossil fuels and the carbon emissions associated with the natural degradation of biomass have accelerated the development of biomass processing technologies. Microwave catalytic pyrolysis is emerging as a technology for efficient conversion of biomass into energy, fuels and chemicals. However, due to the inherent poor dielectric properties and complex composition of biomass, two main technical challenges faced by microwave catalytic pyrolysis of biomass are efficient heating of biomass and improving the selectivity of target products. Potential solutions involve the use of microwave absorbents and catalysts, respectively. This review begins by addressing the difficulty in balancing energy efficiency and conversion efficiency by introducing microwave absorbents that play a positive role in improving heating efficiency. The principle of microwave absorbents in assisting biomass heating is revealed, and the impacts of the microwave absorbent type (related to microwave properties and physical properties) and the additive amount on the heating effect and biomass pyrolysis product distribution are discussed. Subsequently, the search for catalysts applied in biomass microwave pyrolysis for modulation of product distribution is explored. Special attention has been paid to the catalysts with microwave absorption properties, including activated carbon, zeolites, some metal oxides and metal salts. In addition, the energy efficiency, economic feasibility, and environmental impacts of this processing technology utilizing microwave absorbents and catalysts are examined based on energy analysis, techno-economic assessment, and life cycle assessment. The current scale-up challenges of microwave catalytic pyrolysis of biomass and some potential solutions to enhance the commercial feasibility of this technology are also discussed. Finally, the review provides some future development directions of this technology.

Rising greenhouse gas emissions embodied in the global bioeconomy supply chain

The bioeconomy is key to meeting climate targets. Here, we examine greenhouse gas emissions in the global bioeconomy supply chain (1995–2022) using advanced multi-regional input-output analysis and a global land-use change model. Considering agriculture, forestry, land use, and energy, we assess the carbon footprint of biomass production and examine its end-use by provisioning systems. The footprint increased by 3.3 Gt CO2-eq, with 80% driven by international trade, mainly beef and biochemicals (biofuels, bioplastics, rubber). Biochemicals showed the largest relative increase, doubling due to tropical land-use change (feedstock cultivation) and China’s energy-intensive processing. Food from retail contributes most to the total biomass carbon footprint, while food from restaurants and canteens account for >50% of carbon-footprint growth, with three times higher carbon intensity than retail. Our findings emphasize the need for sustainable sourcing strategies and that adopting renewables and halting land-use change could reduce the bioeconomy carbon footprint by almost 60%.

Incubator-integrated electrochemical analysis platform for cell-based studies

Electrochemical analysis methods are critical for probing cellular behavior, offering sensitivity and versatility. However, challenges such as maintaining in-vivo-like conditions during electrochemical testing can affect data accuracy. To address this, we developed an incubator-integrated electrochemical analysis platform that bridges the gap between cell culture and testing environments, preserving physiological conditions. The platform integrates a microfluidic module for cell-electrode interactions, an incubator module for controlled culture and measurement environments, and a measurement module comprising a screen-printed electrode (SPE) and potentiostat for real-time monitoring. Additionally, the sample preparation apparatus ensures robust cell adhesion and mitigates surface degradation during incubation, enabling consistent and reliable measurements. Through extensive studies with cells, we demonstrated the platform’s capability to distinguish varying cell densities and accurately evaluate cell proliferation. Studies on standard-of-care drugs further showcased the platform’s utility in assessing drug efficacy, emphasizing its potential for drug screening and personalized medicine applications.

Molecular optimization using a conditional transformer for reaction-aware compound exploration with reinforcement learning

Designing molecules with desirable properties is a critical endeavor in drug discovery. Because of recent advances in deep learning, molecular generative models have been developed. However, the existing compound exploration models often disregard the important issue of ensuring the feasibility of organic synthesis. To address this issue, we propose TRACER, which is a framework that integrates the optimization of molecular property optimization with synthetic pathway generation. The model can predict the product derived from a given reactant via a conditional transformer under the constraints of a reaction type. The molecular optimization results of an activity prediction model targeting DRD2, AKT1, and CXCR4 revealed that TRACER effectively generated compounds with high scores. The transformer model, which recognizes the entire structures, captures the complexity of the organic synthesis and enables its navigation in a vast chemical space while considering real-world reactivity constraints.

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