Lab-on-a-Chip turns soft : computer-aided, software-enabled microfluidics design

The current practice of designing microfluidic Lab-on-a-Chip (LoCs) limits reusing designs and makes sharing tasks among researchers difficult. One way to achieve that objective is to borrow best practices from engineering. Also it takes a lot of skills to design LoCs. Design-by-assembly in which a LoC can be designed by configuring, laying out subsystems can help new researchers to develop custom chips. Flexible, reusable, and rapid-prototyping-feasible LoC designs can be achieved by fabricated modular microfluidic blocks. However, challenging problems still persist, which limit the usefulness of prefabricated blocks. We propose software microfluidic modules (SoftMABs) based design technique to solve issues fabricated modules face. By configuring SoftMABs, integrating them, the new assembly of SoftMABs can form a 3D LoC design ready to be prototyped. The proposed method can make designing a complex LoC less challenging, and collaborating among laboratories easier. We created SoftMABs and designed a custom microfluidic chip by assembling SoftMABs like LEGOs, dragging-and-dropping them. Later we reconfigured them - by replacing a SoftMAB with another module - to make a new LoC. We believe this computeraided method is an interesting and useful LoC design technique.

Control of crystallization in supramolecular soft materials engineering

As one class of the most important supramolecular functional materials, gels formed by low molecular weight gelators (LMWGs) have many important applications. The key important parameters affecting the in-use performance of a gel are determined by the hierarchical fiber network structures. Fiber networks consisting of weakly interacting multiple domains are commonly observed in gels formed by LMWGs. The rheological properties, particularly the elasticity, of a gel with such a fiber network are weak due to the weak interactions between the individual domains. As achieving desirable rheological properties of such a gel is practically relevant, in this work, we demonstrate the engineering of gels with such a type of fiber network by controlling crystallization of the gelator. Two example gels formed by a glutamic acid derivative in a non-ionic surfactant Tween 80 and in propylene glycol were engineered by controlling the thermodynamic driving force for crystallization. For a fixed gelator concentration, the thermodynamic driving force was manipulated by controlling the temperature for fiber crystallization. It was observed that there exists an optimal temperature at which a gel with maximal elasticity can be fabricated. This will hopefully provide guidelines for producing high performance soft materials by engineering their fiber network structures.

Special issue : recent advances in soft computing : theories and applications

Soft Computing is an interdisciplinary area that encompasses a variety of computing paradigms. Examples of some popular soft computing paradigms include fuzzy computing, neural computing, evolutionary computing, and probabilistic computing. Soft computing paradigms, in general, aim to produce computing systems/machines that exhibit some useful properties, e.g. making inference with vague and/or ambiguous information, learning from noisy and/or incomplete data, adapting to changing environments, and reasoning with uncertainties. These properties are important for the systems/machines to be useful in assisting humans in our daily activities. Indeed, soft computing paradigms have been demonstrated to be capable of tackling a wide range of problems, e.g. optimization, decision making, information processing, pattern recognition, and intelligent data analysis. A number of papers pertaining to some recent advances in theoretical development and practical application of different soft computing paradigms are highlighted in this special issue.

Classification and optimization of product review information using soft computing models

A soft computing framework to classify and optimize text-based information extracted from customers' product reviews is proposed in this paper. The soft computing framework performs classification and optimization in two stages. Given a set of keywords extracted from unstructured text-based product reviews, a Support Vector Machine (SVM) is used to classify the reviews into two categories (positive and negative reviews) in the first stage. An ensemble of evolutionary algorithms is deployed to perform optimization in the second stage. Specifically, the Modified micro Genetic Algorithm (MmGA) optimizer is applied to maximize classification accuracy and minimize the number of keywords used in classification. Two Amazon product reviews databases are employed to evaluate the effectiveness of the SVM classifier and the ensemble of MmGA optimizers in classification and optimization of product related keywords. The results are analyzed and compared with those published in the literature. The outputs potentially serve as a list of impression words that contains useful information from the customers' viewpoints. These impression words can be further leveraged for product design and improvement activities in accordance with the Kansei engineering methodology.