Recovery of Nutrients from the Aqueous Phase of Hydrothermal Liquefaction—A Review

Water · 2025-07-14 · 8 citations

Hydrothermal liquefaction (HTL) is a thermochemical conversion process that converts wet biomass into biocrude oil, a gas phase, a solid phase, and an aqueous phase (HTL-AP). An obstacle to the development and scaling of HTL is the volume of HTL-AP produced during the process, which has high concentrations of nitrogen and carbon and cannot be disposed of in the environment without treatment. The HTL-AP is enriched with organic compounds, particularly light polar organics and nitrogenous compounds, which are inhibitory to microbial treatment in wastewater treatment plants. For this reason, the valorization of the HTL-AP is significant for the circular economy of HTL. This review synthesizes published findings on different types of treatment of the HTL-AP for the recovery of valuable nutrients and the removal of toxic compounds. This work outlines the trade-offs of the treatments to serve as a guide for future research to address these weaknesses and improve the valorization of the HTL-AP. Furthermore, this work uniquely focuses on HTL-AP treatment for recovering plant-available nitrogen, targeting its potential use as a fertilizer. The literature highlights the importance of increasing nitrogen bioavailability in HTL-AP through two-step treatments and by selecting HTL-AP derived from protein-rich feedstocks, which offer higher initial nitrogen content. According to the current state of research, further work is needed to optimize chemical and biological treatments for nutrient recovery from HTL-AP, particularly regarding treatment scale and duration. Additionally, economic analyses across different treatment types are currently lacking, but are essential to evaluate their feasibility and practicality.

The Effects of Cadmium Exposure on Biochemical Indexes, Antioxidant Responses, Inflammation, and Anti‐Stress Responses of Juvenile <scp>GIFT</scp> Tilapia ( <scp> <i>Oreochromis niloticus</i> </scp> )

Environmental Toxicology · 2025-07-08 · 4 citations

ABSTRACT This study investigated the effects of cadmium (Cd 2+ ) exposure on the biochemical indexes, antioxidant responses, non‐specific immune responses, inflammatory responses, anti‐stress responses, and related gene expression levels of juvenile GIFT tilapia ( Oreochromis niloticus ). Four groups of juveniles were cultured for 30 days in four aquaculture waters with different levels of Cd 2+ concentrations (0, 0.2, 0.4, and 0.6 mg/L). Key findings include: In comparison to the control group (0 mg/L Cd 2+ ), cadmium stress significantly impacted the liver and serum biochemical indices of juvenile tilapia ( p &lt; 0.05). The activities of catalase (CAT), superoxide dismutase (SOD), and total antioxidant capacity (T‐AOC) in the gills, liver, and serum were significantly decreased ( p &lt; 0.05) and the contents of malondialdehyde (MDA) showed the opposite trend. The expression levels of interleukin‐10 (IL‐10), sonic hedgehog (SHH), and cytochrome oxidase 1A (CYP1A) were significantly down‐regulated ( p &lt; 0.05) and the expression levels of interleukin‐1β (IL‐1β), tumor necrosis factor α (TNFα), interferon γ (IFNγ), interleukin‐6 (IL‐6), interleukin‐8 (IL‐8), transforming growth factor β1 (TGF‐β1), metallothionein (MT), and heat shock protein 70 (HSP70) in the liver were significantly up‐regulated ( p &lt; 0.05). In conclusion, the concentration of cadmium in aquaculture water exceeded 0.2 mg/L, and the biochemical indexes, antioxidant responses, non‐specific immune responses, inflammatory responses, and anti‐stress responses of juvenile GIFT tilapia were markedly altered. These findings highlight that even sub‐lethal Cd 2+ concentrations (≥ 0.2 mg/L) pose substantial risks to the physiological health and survival of juvenile tilapia.

Financial Viability and Carbon Intensity of Hydrothermal Waste Valorization Systems for Bio-based Asphalt Binder

SSRN Electronic Journal · 2025-01-01

Valorizing Hydrothermal liquefaction aqueous phase via nanofiltration: Enhancing biocrude production from algal biomass

Bioresource Technology · 2025-03-31 · 5 citations

Transcriptional Reprogramming in Trametes Versicolor Reveals Gene Candidates Mediating Resilience to Hydrothermal Liquefaction Aqueous Phase

SSRN Electronic Journal · 2025-01-01

Electrochemical oxidation of hydrothermal liquefaction aqueous phase: optimized degradation, hydrogen generation, and nutrient recovery

Bioresource Technology · 2025-11-27

Waste Biorefinery Concept for Production of Value-Added Products Through Hydrothermal Liquefaction Pathway: A Critical Review and Outlook

ACS ES&T Engineering · 2025-09-23 · 4 citations

Increasing urbanization has led to reliance on fossil fuels, a higher production of waste, and rising greenhouse gas emissions. Sustainable waste management and value-added renewable bioproducts from wet biowaste could be achieved in a biorefinery via a hydrothermal liquefaction (HTL) pathway. Waste biomass is a significant feedstock for potential valorization technologies and is already the single largest source of renewable energy in the United States. HTL’s key advantage over other conversion methods is its ability to directly convert high moisture and nonlipid feedstocks into biocrude at high efficiency. The high biocrude yield with ample carbon and energy content can be upgraded to a wide range of value-added bioproducts, including fuels, chemicals, polymers, and asphaltenes. In this work, a comprehensive review was conducted for this paradigm-shift pathway covering HTL feedstocks, process parameters, biocrude yield and quality, biocrude upgrading and potential bioproducts, and sustainability metrics. Gaps in knowledge were identified, providing a future direction for research to promote waste biorefineries in a circular economy.

Pathologyvlm: a large vision-language model for pathology image understanding

Artificial Intelligence Review · 2025-03-28 · 10 citations

Abstract The previous advancements in pathology image understanding primarily involved developing models tailored to specific tasks. Recent studies have demonstrated that the large vision-language model can enhance the performance of various downstream tasks in medical image understanding. In this study, we developed a domain-specific large vision-language model (PathologyVLM) for pathology image understanding. Specifically, (1) we first construct a human pathology image-text dataset by cleaning the public medical image-text data for domain-specific alignment; (2) Using the proposed image-text data, we first train a pathology language-image pretraining (PLIP) model as the specialized visual encoder to extract the features of pathology image, and then we developed scale-invariant connector to avoid the information loss caused by image scaling; (3) We adopt two-stage learning to train PathologyVLM, first stage for domain alignment, and second stage for end to end visual question &amp; answering (VQA) task. In experiments, we evaluate our PathologyVLM on both supervised and zero-shot VQA datasets, our model achieved the best overall performance among multimodal models of similar scale. The ablation experiments also confirmed the effectiveness of our design. We posit that our PathologyVLM model and the datasets presented in this work can promote research in field of computational pathology. All codes are available at: https://github.com/ddw2AIGROUP2CQUPT/PA-LLaVA

Drop-in fuel production from food waste: system level optimization and analysis

Journal of Cleaner Production · 2025-07-15 · 4 citations

Towards Physics-Based Digital Twins for Enhancing Particulate Tracking Systems in Agri-Food Processing

2025-01-01

<b><sc>Abstract. </sc></b>Despite growing interest in Digital Twins (DTs), their adoption in the agri-food sector remains limited, particularly for complex particulate flows used in many agri-food processes such as unit operations. While simulation efforts exist, developing DTs for these systems requires seamless integration of experimental measurements with numerical simulations. To address this need, this study presents the development and validation of a real-time, non-destructive, volumetric particle tracking system tailored for agri-food applications involving particulate flows. The system employs a synchronized eight-camera setup with GPU-accelerated processing to capture high spatial and temporal resolution 3D trajectories of particles in large-scale domains. The system‘s accuracy is evaluated using a benchmark particulate flow generated by an axisymmetric jet, with reference data from literature serving as the ground truth. Simultaneously, Computational Fluid Dynamics (CFD) simulations are performed under identical conditions. A dual-fold validation is conducted by comparing experimental results and CFD outputs with each other and with the reference dataset. The results show strong agreement in velocity profiles and flow structures across all sources, confirming the accuracy and robustness of the tracking system. This validated framework provides a solid foundation for future integration of experimental and simulation data into a physics-informed DT, enabling advanced monitoring, control, and optimization of particulate processes in the agri-food industry.