![]() ![]() Often experiments have been limited to single questions examining participants general knowledge. Most experiments investigating anchoring effects have been limited in their design and implementation. Typically, anchoring bias occurs when numeric anchors are provided, although some research has also investigated the effect of non-numeric anchors. This effect is pervasive and robust in a variety of experimental settings (see and real-world contexts, including in courtroom sentencing, in negotiations, in financial market decisions, in property pricing, and in judging the probability of the outbreak of a nuclear war. The term ‘anchoring’ can therefore be understood as people’s tendency to rely heavily on these prior values (or ‘anchors’) when making decisions. the value of a car), the resulting judgement tends to be similar to a previously encountered value (e.g. ![]() Tversky and Kahneman observed that in situations where people make estimates or predictions (e.g. This paper examines one of these effects: anchoring. show that depending on what online platform is used, people may be exposed to extremely different information, which can, in turn, result in the development of “social bubbles” or even changes in their emotional state. Given the black-box nature of the algorithms that drive searching and selecting relevant information on the Internet, it is left to large Internet corporations managing those algorithms to decide which information is “valuable” and therefore displayed to users. Examples of cognitive bias include the anchoring effect (that is, the influence on decisions of the first piece of information encountered), the availability heuristic (where estimates of the probability of an outcome depend on ease of access to that information), the framing effect (the presentation of identical information in different ways), and confirmation bias (a focus on information that supports a pre-existing position), among others. Commonly referred to as cognitive biases, these errors are the result of non-rational information processing. However, as has been shown by Tversky and Kahneman these shortcuts come at a cost: to be able to quickly solve a problem, certain information will be simplified, some ignored, and estimations will be made, thus increasing the likelihood of systematic errors in decisions. Our combined experimental and theoretical analyses rationalized the use of nanodot catalysts in high energy rechargeable batteries.Heuristics are mental shortcuts that enable us to arrive at solutions to complex tasks or problems with minimal effort. In the presence of a small amount of 1T MoS 2 nanodots, porous carbon/Li 2S 6 cathodes exhibited remarkable electrochemical performance retaining a capacity of 9.3 mA h cm −2 over 300 cycles under high sulfur loading of 12.9 mg cm −2 and a low electrolyte/sulfur ratio of 4.6 μL mg −1, which rivals the performance of the state-of-the-art LSBs. First-principle calculations indicated that the surface and Mo-terminated edges of 1T MoS 2 provide stronger anchor sites for Li 2S, a lower Li–S decomposition barrier, and faster Li ion migration than those for the 2H phase, which suggest the unique catalytic property for edge-rich 1T MoS 2 nanodots in LSBs. Electrochemical and synchrotron in situ X-ray diffraction characterizations revealed that 1T MoS 2 nanodots with numerous active sites are favored to trap and propel the redox reactions for polysulfides. ![]() Herein, we explore 1T MoS 2 nanodots as powerful electrocatalyst to overcome this issue. A high sulfur loading and low electrolyte/sulfur ratio are considered prerequisites for practical high-energy lithium sulfur batteries (LSBs) however, shuttling and the sluggish conversion of flooded polysulfides make it challenging to achieve the full utilization of active materials with an extended cyclic life. ![]()
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