This review exhaustively evaluates the role of nanomaterials across the synthesis, characterization and application stages of biofuel systems. Common types of nanomaterials that are used for biofuel applications include metal oxides, carbon-based structures, and hybrids, which are evaluated for their effectiveness in efficient biofuel production. The properties of such nanomaterials are being utilized as an aid to produce biofuels through improved catalysis, enzyme immobilization and thermal stability. Common synthesis methods, such as sol-gel, coprecipitation, and green synthesis, are compared, alongside characterization tools, such as TEM, SEM, FTIR, and BET. This study focuses on transesterification, biomass pretreatment, and fermentation processes, where nanomaterials significantly improve yield and reusability. There are several challenges, despite the merits of using nanomaterials, and the trade-offs include cost, scalability, and environmental impact, which further expand into evaluating the life cycle of such materials. This review outlines the practical potential of nanomaterials in enabling efficient and sustainable biofuel production.
Selected applications of novel techniques in Agricultural Biotechnology, Health Food formulations and Medical Biotechnology are being reviewed with the aim of unraveling future developments and policy changes that are likely to open new niches for Biotechnology and prevent the shrinking or closing the existing ones. Amongst the selected novel techniques with applications to both Agricultural and Medical Biotechnology are: immobilized bacterial cells and enzymes, microencapsulation and liposome production, genetic manipulation of microorganisms, development of novel vaccines from plants, epigenomics of mammalian cells and organisms, as well as biocomputational tools for molecular modeling related to disease and Bioinformatics. Both fundamental and applied aspects of the emerging new techniques are being discussed in relation to their anticipated impact on future biotechnology applications together with policy changes that are needed for continued success in both Agricultural and Medical Biotechnology. Several novel techniques are illustrated in an attempt to convey the most representative and powerful tools that are currently being developed for both immediate and long term applicat
The biotechnology industry poses challenges and possibilities for startups and small businesses. It is characterized by high charges and complex policies, making it difficult for such agencies to set up themselves. However, it additionally offers avenues for innovation and increase. This paper delves into powerful techniques that can be a resource in managing biotechnology innovation, which includes partnerships, highbrow assets improvement, virtual technologies, customer engagement, and government investment. These strategies are important for fulfillment in an industry that is constantly evolving. By embracing agility and area of interest focus, startups and small companies can successfully compete in this dynamic discipline.
The escalating cost, extended timelines, and low success rates in pharmaceutical research demand a fundamental rethinking of biotechnology R&D infrastructure. This chapter introduces the concept of the AI-Integrated Biotechnology Hub, a purpose-built research ecosystem uniting residential, commercial, clinical, and research facilities under a central, AI-driven operating system. Designed as a multi-sided platform, the hub leverages continuous, multi-modal health data collection, advanced smart living environments, and federated learning models to enable secure, privacy-preserving biomedical research. By integrating real estate, biotechnology facilities, research hospitals, and community services, the model maximizes data utility, accelerates drug discovery, and enhances resident well-being. Transparency, accountability, and ethical stewardship are critical pillars of governance, enacted through dynamic consent, data trusts, and multi-stakeholder oversight. Scalable across urban and vertical architectures, this paradigm offers a viable, sustainable pathway toward improving healthspan, fostering innovation, and reshaping the economics of global drug development.
This study investigates the energy valorization potential of 16 aromatic seed residues (ASW), a by-product generated after essential oil extraction from Mediterranean aromatic and medicinal plants. Driven by the increasing demand for natural bioactive ingredients and the expansion of aromatic crop production, large amounts of residual biomass remain underutilized. Their incorporation into thermochemical conversion routes aligns with circular economy strategies, offering opportunities for renewable energy generation, waste minimization, and the development of value-added bioenergy products. The objective of this work is to provide a comprehensive thermochemical, elemental, and structural assessment of ASW to determine their suitability for solid biofuel production (pellets, briquettes), pyrolysis for bio-oil generation, and biochar applications. The samples were analyzed under standardized ISO methodologies to ensure comparability and adherence to industrial fuel quality requirements.
Digital twins (DTs) are revolutionizing the biotechnology industry by enabling sophisticated digital representations of biological assets, microorganisms, drug development processes, and digital health applications. However, digital twinning at micro and nano scales, particularly in modeling complex entities like bacteria, presents significant challenges in terms of requiring advanced Internet of Things (IoT) infrastructure and computing approaches to achieve enhanced accuracy and scalability. In this work, we propose a novel framework that integrates the Internet of Bio-Nano Things (IoBNT) with advanced machine learning techniques, specifically convolutional neural networks (CNN) and federated learning (FL), to effectively tackle the identified challenges. Within our framework, IoBNT devices are deployed to gather image-based biological data across various physical environments, leveraging the strong capabilities of CNNs for robust machine vision and pattern recognition. Subsequently, FL is utilized to aggregate insights from these disparate data sources, creating a refined global model that continually enhances accuracy and predictive reliability, which is crucial for the effecti
Precision fermentation relies on microbial cell factories to produce sustainable food, pharmaceuticals, chemicals, and biofuels. Specialized laboratories such as biofoundries are advancing these processes using high-throughput bioreactor platforms, which generate vast datasets. However, the lack of community standards limits data accessibility and interoperability, preventing integration across platforms. In order to address this, we introduce PREFER, an open-source ontology designed to establish a unified standard for bioprocess data. Built in alignment with the widely adopted Basic Formal Ontology (BFO) and connecting with several other community ontologies, PREFER ensures consistency and cross-domain compatibility and covers the whole precision fermentation process. Integrating PREFER into high-throughput bioprocess development workflows enables structured metadata that supports automated cross-platform execution and high-fidelity data capture. Furthermore, PREFER's standardization has the potential to bridge disparate data silos, generating machine-actionable datasets critical for training predictive, robust machine learning models in synthetic biology. This work provides the f
Abatement options for the hard-to-electrify parts of the transport sector are needed to achieve ambitious emissions targets. Biofuels based on biomass, electrofuels based on renewable hydrogen and a carbon source, as well as fossil fuels compensated by carbon dioxide removal (CDR) are the main options. Currently, biofuels are the only renewable fuels available at scale and are stimulated by blending mandates. Here, we estimate the system cost of enforcing such mandates in addition to an overall emissions cap for all energy sectors. We model overnight scenarios for 2040 and 2060 with the sector-coupled European energy system model PyPSA-Eur-Sec, with a high temporal resolution. The following cost drivers are identified: (i) high biomass costs due to scarcity, (ii) opportunity costs for competing usages of biomass for industry heat and combined heat and power (CHP) with carbon capture, and (iii) lower scalability and generally higher cost for biofuels compared to electrofuels and fossil fuels combined with CDR. With a -80% emissions reduction target in 2040, variable renewables, partial electrification of heat, industry and transport and biomass use for CHP and industrial heat are im
Potential strategies for the development and large-scale application of renewable energy sources aimed at reducing the usage of carbon-based fossil fuels are assessed here, especially in the event of the abandonment of such fuels. The aim is to aid the initiative to reduce the harmful effects of carbon-based fossil fuels on the environment and ensure a reduction in greenhouse gases and sustainability of natural resources. Small-scale renewable energy application for heating, cooling, and electricity generation in households and commercial buildings are already underway around the world. Hydrogen (H2) and ammonia (NH3), which are presently produced using fossil fuels, already have significant applications in society and industry, and are therefore good candidates for large-scale production through the use of renewable energy sources. This will help to reduce the greenhouse gas emissions appreciably around the world. While the first-generation biofuels production using food crops may not be suitable for long-range fuel production, due to competition with the food supply, the 2nd, 3rd and 4th generation biofuels have the potential to produce large, worldwide supplies of fuels. Product
Renewable liquid fuels are essential for achieving emissions targets for hard-to-electrify sectors such as aviation and shipping. While biofuels and synthetic e-fuels have been well-studied, e-biofuels, produced by adding renewable hydrogen to biomass conversion to better utilise the biogenic carbon, remain understudied and lack a clear role in EU fuel regulations. In this paper, using a sector-coupled European energy system model, we find that e-biofuels are cost-effective to meet stringent emissions targets if biomass availability is limited and fossil fuels are ineligible, either due to limited carbon sequestration capacity or to high renewable fuel mandates. By directly increasing utilisation of biogenic carbon instead of synthesising fuels based on captured $CO_2$, there are savings from fuel production and carbon capture that reduce total system costs by up to 2.7% and liquid fuel costs by more than 10%. Our results highlight the role of e-biofuels as a potential hedge against uncertainty in biomass, hydrogen, and carbon storage availability, as well as evolving policy implementation.
The technical and economic feasibility to deliver sustainable liquid biocrude through hydrothermal liquefaction (HTL) while enabling negative carbon dioxide emissions is evaluated in this paper, looking into the potential of the process in the context of negative emission technologies (NETs) for climate change mitigation. In the HTL process, a gas phase consisting mainly of carbon dioxide is obtained as a side product driving a potential for the implementation of carbon capture and storage in the process (BECCS) that has not been explored yet in the existing literature and is undertaken in this study. To this end, the process is divided in a standard HTL base and a carbon capture add-on, having forestry residues as feedstock. The Selexol technology is adapted in a novel scheme to simultaneously separate the CO2 from the HTL gas and recover the excess hydrogen for biocrude upgrading. The cost evaluation indicates that the additional cost of the carbon capture can be compensated by revenues from the excess process heat and the European carbon allowance market. The impact in the MFSP of the HTL base case ranges from -7% to 3%, with -15% in the most favorable scenario, with a GHG emiss
Driven by advancements in high-throughput biological technologies and the growing number of sequenced genomes, the construction of in silico models at the genome scale has provided powerful tools to investigate a vast array of biological systems and applications. Here, we review comprehensively the uses of such models in industrial and medical biotechnology, including biofuel generation, food production, and drug development. While the use of in silico models is still in its early stages for delivering to industry, significant initial successes have been achieved. For the cases presented here, genome-scale models predict engineering strategies to enhance properties of interest in an organism or to inhibit harmful mechanisms of pathogens. Going forward, genome-scale in silico models promise to extend their application and analysis scope to become a transformative tool in biotechnology.
Elements of experimental equipment for the production of biofuel components must meet high reliability and safety requirements. At the same time, in the course of research on the subject of creating equipment for the production of biofuels, a variable range of equipment is regularly proposed and should be checked. The manufacture of elements of such equipment by traditional methods is expensive and inefficient, time-consuming, which negatively affects the speed of scientific research. To this end, it is proposed to develop a robotic 3D printing complex that provides maximum flexibility in creating mock-ups and test samples of equipment for the production of biofuel components. The article discusses the experience of successfully creating equipment elements for the production of fuels using 3d printing. Next, the choice of a robotization scheme for a 3D printing installation is described and the choice of printing technology is substantiated. The article also presents the results of calculating the parameters of the 3v-printer robot and the results of calculating the similarity parameters for the implementation and evaluation of control algorithms. The results of a numerical experim
In the past few decades, the life sciences have experienced an unprecedented accumulation of data, ranging from genomic sequences and proteomic profiles to heavy-content imaging, clinical assays, and commercial biological products for research. Traditional static databases have been invaluable in providing standardized and structured information. However, they fall short when it comes to facilitating exploratory data interrogation, real-time query, multidimensional comparison and dynamic visualization. Integrated data-driven research environments aiming at supporting user-driven data queries and visualization offer promising new avenues for making the best use of the vast and heterogeneous data streams collected in biological research. This perspective article discusses the potential of interactive and integrated frameworks, highlighting the importance of implementing this model in biotechnology research, while going through the state-of-the-art in database design, technical choices behind modern data management systems, and emerging needs in multidisciplinary research. Special attention is given to data interrogation strategies, user interface design, and comparative analysis capa
Microfluidic droplet screens serve as an innovative platform for high-throughput biotechnology, enabling significant advancements in discovery, product optimization, and analysis. This review sheds light on the emerging trend of interaction assays in microfluidic droplets, underscoring the unique suitability of droplets for these applications. Encompassing a diverse range of biological entities such as antibodies, enzymes, DNA, RNA, various microbial and mammalian cell types, drugs, and other molecules, these assays demonstrate their versatility and scope. Recent methodological breakthroughs have escalated these screens to novel scales of bioanalysis and biotechnological product design. Moreover, we highlight pioneering advancements that extend droplet-based screens into new domains: cargo delivery within human bodies, application of synthetic gene circuits in natural environments, 3D-printing, and the development of droplet structures responsive to environmental signals. The potential of this field is profound and only set to increase.
We present a detailed study of the Circinus Galaxy, investigating its star formation, dust and gas properties both in the inner and outer disk. To achieve this, we obtained high-resolution Spitzer mid-infrared images with the IRAC (3.6, 5.8, 4.5, 8.0 micron) and MIPS (24 and 70 micron) instruments and sensitive HI data from the Australia Telescope Compact Array (ATCA) and the 64-m Parkes telescope. These were supplemented by CO maps from the Swedish-ESO Submillimetre Telescope (SEST). Because Circinus is hidden behind the Galactic Plane, we demonstrate the careful removal of foreground stars as well as large- and small-scale Galactic emission from the Spitzer images. We derive a visual extinction of Av = 2.1 mag from the Spectral Energy Distribution of the Circinus Galaxy and total stellar and gas masses of 9.5 x 10^{10} Msun and 9 x 10^9 Msun, respectively. Using various wavelength calibrations, we find obscured global star formation rates between 3 and 8 Msun yr^{-1}. Star forming regions in the inner spiral arms of Circinus, which are rich in HI, are beautifully unveiled in the Spitzer 8 micron image. The latter is dominated by polycyclic aromatic hydrocarbon (PAH) emission from
In this paper, we propose an innovative investment framework incorporating asset allocation and class diversification oriented specifically for the biotechnology industry. With growing interests and capitalization in multiple biotech markets, investors require a more dynamic method of managing their assets within individual portfolios for optimal return efficiency. By selecting a single firm representative of identified industry trends, analyzing financial metrics relevant to the suggested approaches, and assessing financial health, we developed an adaptable investment methodology. We also performed analyses of industrial viability and investigated the implications of the selected strategies, with which we were able to optimize our framework for versatile application within specialized biotech markets.
Viruses are the most abundant biological entities on Earth and play central roles in shaping microbiomes and influencing ecosystem functions. Yet, most viral genes remain uncharacterized, comprising what is commonly referred to as "viral dark matter." Metagenomic studies across diverse environments consistently show that 40-90% of viral genes lack known homologs or annotated functions. This persistent knowledge gap limits our ability to interpret viral sequence data, understand virus-host interactions, and assess the ecological or applied significance of viral genes. Among the most intriguing components of viral dark matter are auxiliary viral genes (AVGs), including auxiliary metabolic genes (AMGs), regulatory genes (AReGs), and host physiology-modifying genes (APGs), which may alter host function during infection and contribute to microbial metabolism, stress tolerance, or resistance. In this review, we explore recent advances in the discovery and functional characterization of viral dark matter. We highlight representative examples of novel viral proteins across diverse ecosystems including human microbiomes, soil, oceans, and extreme environments, and discuss what is known, and
Biotechnology Industry 5.0 is advancing with the integration of cutting-edge technologies like Machine Learning (ML), the Internet Of Things (IoT), and cloud computing. It is no surprise that an industry that utilizes data from customers and can alter their lives is a target of a variety of attacks. This chapter provides a perspective of how Machine Learning Security Operations (MLSecOps) can help secure the biotechnology Industry 5.0. The chapter provides an analysis of the threats in the biotechnology Industry 5.0 and how ML algorithms can help secure with industry best practices. This chapter explores the scope of MLSecOps in the biotechnology Industry 5.0, highlighting how crucial it is to comply with current regulatory frameworks. With biotechnology Industry 5.0 developing innovative solutions in healthcare, supply chain management, biomanufacturing, pharmaceuticals sectors, and more, the chapter also discusses the MLSecOps best practices that industry and enterprises should follow while also considering ethical responsibilities. Overall, the chapter provides a discussion of how to integrate MLSecOps into the design, deployment, and regulation of the processes in biotechnology
The number of people able to end Earth's technical civilization has heretofore been small. Emerging dual-use technologies, such as biotechnology, may give similar power to thousands or millions of individuals. To quantitatively investigate the ramifications of such a marked shift on the survival of both terrestrial and extraterrestrial technical civilizations, this paper presents a two-parameter model for civilizational lifespans, i.e. the quantity $L$ in Drake's equation for the number of communicating extraterrestrial civilizations. One parameter characterizes the population lethality of a civilization's biotechnology and the other characterizes the civilization's psychosociology. $L$ is demonstrated to be less than the inverse of the product of these two parameters. Using empiric data from Pubmed to inform the biotechnology parameter, the model predicts human civilization's median survival time as decades to centuries, even with optimistic psychosociological parameter values, thereby positioning biotechnology as a proximate threat to human civilization. For an ensemble of civilizations having some median calculated survival time, the model predicts that, after 80 times that dura