Annual review of chemical and biomolecular engineering最新文献

Advances in experimental synthesis and computer simulations have led to the proliferation of anisotropy and particle geometry as popular handles for directed self-assembly. This paradigm employs entropy to direct building block organization into desired spatial and orientational orderings. Yet, how does a metric associated primarily with disorder give rise to ordered assemblies? We first explain the governing principles behind entropic crystallization and entropy maximization processes. We then show how entropic forces can produce emergent, attractive, and bond-like interactions between otherwise sterically repulsive particles. Building on these ideas, we establish entropy as a mediator of interparticle attraction in hard particle systems that relies on extrinsic, systems-level behaviors as opposed to intrinsic, particle-level properties. Finally, we present a theory of entropic bonding that formalizes the phenomena discussed into a rigorous mathematical framework and discuss relevant next steps for its development and applications of entropic crystallization in materials design.

Understanding the molecular, cellular, and physiological components of neurodegenerative diseases (NDs) is paramount for developing accurate diagnostics and efficacious therapies. However, the complexity of ND pathology and the limitations associated with conventional analytical methods undermine research. Fortunately, microfluidic technology can facilitate discoveries through improved biomarker quantification, brain organoid culture, and small animal model manipulation. Because this technology can increase experimental throughput and the number of metrics that can be studied in concert, it demands more sophisticated computational tools to process and analyze results. Advanced analytical algorithms and machine learning platforms can address this challenge in data generated from microfluidic systems, but they can also be used outside of devices to discern patterns in genomic, proteomic, anatomical, and cognitive data sets. We discuss these approaches and their potential to expedite research discoveries and improve clinical outcomes through ND characterization, diagnosis, and treatment platforms.

Modeling is an indispensable tool for understanding and improving the growth of bulk, single crystals. Such crystals are required for the fabrication of the electronic and photonic devices that enable information technology, communications, sensing, solid-state lighting, solar energy production, and many other applications. These materials are much more than simply very pure, specialty chemicals. They must meet strict requirements for solid-state structural perfection and must be produced with high yields and low costs. Successful manufacturing techniques have been developed that utilize thermodynamic phase change to solidify a high-temperature melt into a crystal of high quality. However, harsh conditions and batch operation limit both diagnostic measurements and data available to connect growth conditions to outcomes, making modeling even more important for process improvement. Challenges and opportunities are discussed for melt crystal growth processes, with research examples that demonstrate how modeling has provided important insight into crystal-melt interface shape, dopant segregation, morphological instability, and defect formation.

My career has not been straightforward. Although I am a chemical engineer, and I'm proud of that, I took a path from chemistry and engineering to one that also involved experimental biology and medicine. This was very unusual many decades ago. In so doing, I met with rejection and ridicule early in my career. However, by going down that path, I was able to make discoveries and inventions that I hope have saved and improved lives, and I've been able to train a great number of people who are going down the road I began traveling over many years ago.

Chirality, a fundamental attribute of asymmetry, pervades in both nature and functional soft materials. In chiral material systems design, achieving global symmetry breaking of building blocks during assembly, with or without the aid of additives, has emerged as a promising strategy across domains including chiral sensing, electronics, photonics, spintronics, and biomimetics. We first introduce the fundamental aspects of chirality, including its structural basis and symmetry-breaking mechanisms considering free energy minimization. We particularly emphasize supramolecular assembly, such as through the formation of chiral liquid crystal phases. Next, we summarize processing strategies to control chiral symmetry breaking, exploiting external fields such as flow, magnetic fields, and templates. The final section discusses interactions between chiral molecular assemblies with circularly polarized (CP) light and electronic spin and their applications in CP light detectors, CP-spin-organic light-emitting diodes, CP displays, and spintronic devices based on the chirality-induced spin selectivity effect.

Various technologies and strategies have been proposed to decarbonize the chemical industry. Assessing the decarbonization, environmental, and economic implications of these technologies and strategies is critical to identifying pathways to a more sustainable industrial future. This study reviews recent advancements and integration of systems analysis models, including process analysis, material flow analysis, life cycle assessment, techno-economic analysis, and machine learning. These models are categorized based on analytical methods and application scales (i.e., micro-, meso-, and macroscale) for promising decarbonization technologies (e.g., carbon capture, storage, and utilization, biomass feedstock, and electrification) and circular economy strategies. Incorporating forward-looking, data-driven approaches into existing models allows for optimizing complex industrial systems and assessing future impacts. Although advances in industrial ecology-, economic-, and planetary boundary-based modeling support a more holistic systems-level assessment, more efforts are needed to consider impacts on ecosystems. Effective applications of these advanced, integrated models require cross-disciplinary collaborations across chemical engineering, industrial ecology, and economics.

Hydrogen is similar to natural gas in terms of its physical and chemical properties but does not release carbon dioxide when burnt. This makes hydrogen an energy carrier of great importance in climate policy, especially as an enabler of increasing integration of volatile renewable energy, progressive electrification, and effective emission reductions in the hard-to-decarbonize sectors. Leaving aside the problems of transporting hydrogen as a liquid, technological challenges along the entire supply chain can be considered as solved in principle, as shown in the experimental findings of the Hydrogen Innovation Program of the German Technical and Scientific Association for Gas and Water. By scaling up production and end-use capacities and, most importantly, producing hydrogen in regions with abundant renewable energy, hydrogen and its applications can displace natural gas at affordable prices in the medium term. However, this substitution will take place at different rates in different regions and with different levels of added value, all of which must be understood for hydrogen uptake to be successful.

Helicobacter pylori infections are a major cause of peptic ulcers and gastric cancers. The development of robust inflammation in response to these flagellated, motile bacteria is correlated with poor prognosis. Chemotaxis plays a crucial role in H. pylori colonization, enabling the bacteria to swim toward favorable chemical environments. Unlike the model species of bacterial chemotaxis, Escherichia coli, H. pylori cells possess polar flagella. They run forward by rotating their flagella counterclockwise, whereas backward runs are achieved by rotating their flagella clockwise. We delve into the implications of certain features of the canonical model of chemotaxis on our understanding of biased migration in polarly flagellated bacteria such as H. pylori. In particular, we predict how the translational displacement of H. pylori cells during a backward run could give rise to chemotaxis errors within the canonical framework. Also, H. pylori lack key chemotaxis enzymes found in E. coli, without which sensitive detection of ligands with a wide dynamic range seems unlikely. Despite these problems, H. pylori exhibit robust ability to migrate toward urea-rich sources. We emphasize various unresolved questions regarding the biophysical mechanisms of chemotaxis in H. pylori, shedding light on potential directions for future research. Understanding the intricacies of biased migration in H. pylori could offer valuable insights into how pathogens breach various protective barriers in the human host.