“…Pressure-responsive materials have received significant attention by both academic and industrial communities for biomedical [8], automotive [9], and aerospace [10] utilizations. These materials are generally composed by an inorganic [11,12] or polymeric [13] stretchable/compressible matrix and a dispersed conductive filler. For these applications, the most widely used fillers are carbon-based species, such as carbon nanotubes [14], as well as graphene and graphene-like materials [15].…”
The development of responsive composite materials is among the most interesting challenges in contemporary material science and technology. Nevertheless, the use of highly expensive nanostructured fillers has slowed down the spread of these smart materials in several key productive sectors. Here, we propose a new piezoresistive PVA composite containing a cheap, conductive, waste-derived, cotton biochar. We evaluated the electromagnetic properties of the composites under both AC and DC regimes and as a function of applied pressure, showing promisingly high conductivity values by using over 20 wt.% filler loading. We also measured the conductivity of the waste cotton biochar from 20 K up to 350 K observing, for the first time, hopping charge transport in biochar materials.
“…Pressure-responsive materials have received significant attention by both academic and industrial communities for biomedical [8], automotive [9], and aerospace [10] utilizations. These materials are generally composed by an inorganic [11,12] or polymeric [13] stretchable/compressible matrix and a dispersed conductive filler. For these applications, the most widely used fillers are carbon-based species, such as carbon nanotubes [14], as well as graphene and graphene-like materials [15].…”
The development of responsive composite materials is among the most interesting challenges in contemporary material science and technology. Nevertheless, the use of highly expensive nanostructured fillers has slowed down the spread of these smart materials in several key productive sectors. Here, we propose a new piezoresistive PVA composite containing a cheap, conductive, waste-derived, cotton biochar. We evaluated the electromagnetic properties of the composites under both AC and DC regimes and as a function of applied pressure, showing promisingly high conductivity values by using over 20 wt.% filler loading. We also measured the conductivity of the waste cotton biochar from 20 K up to 350 K observing, for the first time, hopping charge transport in biochar materials.
“…, respectively. ℃ Based on the excellent high-temperature stability, chemical durability, and piezoresistivity, the PDCs are expected to be ideal candidates for in situ pressure sensors used in gas turbine engines [60,68,358,361,362]. For instance, Shao et al [363] prepared a SiBCN ceramic pressure sensor and tested its performance using half Wheatstone bridge.…”
Since the 1960s, a new class of Si-based advanced ceramics called polymer-derived ceramics (PDCs) has been widely reported because of their unique capabilities to produce various ceramic materials (e.g., ceramic fibers, ceramic matrix composites, foams, films, and coatings) and their versatile applications. Particularly, due to their promising structural and functional properties for energy conversion and storage, the applications of PDCs in these fields have attracted much attention in recent years. This review highlights the recent progress in the PDC field with the focus on energy conversion and storage applications. Firstly, a brief introduction of the Si-based polymer-derived ceramics in terms of synthesis, processing, and microstructure characterization is provided, followed by a summary of PDCs used in energy conversion systems (mainly in gas turbine engines), including fundamentals and material issues, ceramic matrix composites, ceramic fibers, thermal and environmental barrier coatings, as well as high-temperature sensors. Subsequently, applications of PDCs in the field of energy storage are reviewed with a strong focus on anode materials for lithium and sodium ion batteries. The possible applications of the PDCs in Li-S batteries, supercapacitors, and fuel cells are discussed as well. Finally, a summary of the reported applications and perspectives for future research with PDCs are presented.
“…Here, however, we will instead focus on materials that have been engineered to be selfsensing; that is, an additional constituent has been added to the material system without which it does not exhibit piezoresistivity. This is most commonly done by adding a conductive phase to a non-conductive matrix such as polymers (including structural polymers such as epoxy vinyl ester [250], polymeric thin films for use as sensing skins [251], laser-induced graphene inter-layers in continuous fibre composites [252,253] and even polymer binders in energetic materials [254,255]), cements [256] or ceramics [257]. Electrical transport is then a consequence of percolation-the composite conducts electricity when enough fillers have been added to form an electrically connected network.…”
The field of structural engineering is vast, spanning areas from the design of new infrastructure to the assessment of existing infrastructure. From the onset, traditional entry-level university courses teach students to analyse structural responses given data including external forces, geometry, member sizes, restraint, etc.—characterizing a
forward
problem (structural causalities
→
structural response). Shortly thereafter, junior engineers are introduced to structural design where they aim to, for example, select an appropriate structural form for members based on design criteria, which is the
inverse
of what they previously learned. Similar inverse realizations also hold true in structural health monitoring and a number of structural engineering sub-fields (response
→
structural causalities). In this light, we aim to demonstrate that many structural engineering sub-fields may be fundamentally or partially viewed as
inverse problems
and thus benefit via the rich and established methodologies from the inverse problems community. To this end, we conclude that the future of inverse problems in structural engineering is inexorably linked to engineering education and machine learning developments.
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